Proceedings of 2019 Chinese Intelligent Automation Conference [1st ed. 2020] 978-981-32-9049-5, 978-981-32-9050-1

Table of contents :
Front Matter ....Pages i-xi
Research on Combat Deduction Platform Technology for Intelligent Operational Decision (Xin Liao, Zheng-hao Sun)....Pages 1-13
Study of the Auxiliary Robot Used to Disassemb and Assemb Mid-Set Switch Cubicle Based on BCI (Weiwei Huang, Bihui Zhang, Rui Li)....Pages 14-21
Ship Detection Based on Faster R-CNN Network in Optical Remote Sensing Images (Min Zhai, Huaping Liu, Fuchun Sun, Yan Zhang)....Pages 22-31
An Efficient Real-Time Indoor Autonomous Navigation and Path Planning System for Drones Based on RGB-D Sensor (Ran Xiao, Hao Du, Chaowen Xu, Wei Wang)....Pages 32-44
Research on Port Throughput Prediction of Tianjin Port Based on PCA-SVR in the New Era (Jinyu Wei, Yuqiao Tang, Yang Yu, Xueshan Sun)....Pages 45-52
Research on Automatic Detection Method of Part Shape Based on Projection Optics and Image Processing (Xiangyang Sun, Binggao He, Can Wang, Yuegang Fu)....Pages 53-59
Signal Detection, Processing and Challenges of Non-invasive Brain-Computer Interface Technology (Xiaoyuan Li, Feng Chen, Yaohui Jia, Xinyu Liu)....Pages 60-67
An Iterative Model from Grid Cells to Place Cells (Naigong Yu, Yishen Liao, Xiangguo Zheng, Hui Feng)....Pages 68-78
Visual Stabilization of Wheeled Mobile Robots with Unknown Visual Parameters (Yao Huang, Jianbo Su)....Pages 79-87
A Game Model for Gomoku Based on Deep Learning and Monte Carlo Tree Search (Xiali Li, Shuai He, Licheng Wu, Daiyao Chen, Yue Zhao)....Pages 88-97
Cascading Failure Analysis of Military Command and Control Network Based on SIS Model (Jichao Xing, Zhaoliang Zhu, Chuxiang Chen, Xiaofeng Guo)....Pages 98-105
Laser Scan Matching in Polar Coordinates Using Gaussian Process (Yinqiang Wang, Bo Li, Bo Han, Yu Zhang, Wenjie Zhao)....Pages 106-115
An Evolutionary Membrane Algorithm Based on Competition Mechanism for Multi-objective Optimization Problems (Zhiqiang Geng, Yunfei Cui, Yongming Han)....Pages 116-123
Design of Intelligent Measuring Device for On-line Machining Parts of Lathe (Xiangyang Sun, Binggao He, Lijuan Shi, Can Wang, Yuegang Fu)....Pages 124-130
Algorithm Research on Optimizing Ordering and Pricing Policy for Perishable Items (Xin Yang, Yuan Zhao, Jin-yu Wei, Yang Yu)....Pages 131-137
Facial Expression Recognition System Based on Deep Residual Fusion Neural Network (Haonan Wang, Junhang Ding, Fan Wang, Zhe Ma)....Pages 138-144
Research and Application on Ensemble Learning Methods (Yuzhong Wang)....Pages 145-155
Optimal Fault Tolerant Control of Five-Phase Permanent Magnet Synchronous Motor Under One-Phase Open-Circuit Fault (Song Jie)....Pages 156-165
Enhanced Pulse Density Modulation for Efficiency Optimization in Inductive Power Transfer Systems (Hong Zheng, Rui Bian, Yubing Gu)....Pages 166-173
RGB-D Saliency Object Detection Based on Adaptive Manifolds Filtering (Lingling Zi, Xin Cong, Yanfei Peng, Xitao Chen)....Pages 174-181
Study of Speed Stabilization Loop for Airborne Photoelectric Platform Based on Active Disturbance Rejection Control (Yongli Bi, Shigang Wang)....Pages 182-190
Active Disturbance Rejection Control of Drum Water Level with Generalized Extended State Observer (Cuiping Pu, Jie Ren, Jianbo Su)....Pages 191-199
Improved Cuckoo Search Algorithm Based on Exponential Function (Kun Wang, Xiaofeng Lian, Bing Pan)....Pages 200-207
Autonomous Intelligent Control for Path Following of Unmanned Surface Vessels with Input Constraints (Yalei Yu, Chen Guo, Haomiao Yu)....Pages 208-215
Investigation on Energy Feedback Potentiality of New Hydraulic Interconnected Energy-Regenerative Suspension (Zeyu Sun, Ruochen Wang, Xiangpeng Meng, Qiuiming Jiang)....Pages 216-225
Sliding Mode Control with Uncertain Model for a Quadrotor UAV’s Automatic Visual Landing Problem (Qing Fei, Jiaxiang Zhang, Zhengyang Wang, Xiaosong Huang)....Pages 226-233
Depth-Fusion Based on Gaussian Mixture Model for RGB-D Visual SLAM (Zhaotong Ding, Ran Huang, Biao Hu)....Pages 234-242
Error Analysis of Dual Antenna UAV Tracking System (Shujuan Li, Junhang Ding, Jianzhi Li)....Pages 243-250
Fault Tolerant Control Allocation Based on Adaptive Sliding Mode Control for Distributed-Driven Electric Vehicle (Guohai Liu, Shuangjian Wang, Duo Zhang, Yue Shen, Zhen Yao)....Pages 251-261
Research on Sliding Mode Active Disturbance Rejection Control and Thrust Allocation of Dynamic Positioning System (Zaiji Piao, Chen Guo)....Pages 262-269
Research on Indoor Positioning Method Based on Improved HS-AlexNet Model (Libiao Zhang, Rui Zhao, Yuqing Liu, Xinyu Yang, Shipeng Li)....Pages 270-277
A Modified Energy and Signal Coordination Control Strategy for a Robotic System (Yu Wang, Haisheng Yu, Jinpeng Yu, Herong Wu, Xudong liu)....Pages 278-286
Parameter Optimization Control of Multiple Response Process Based on Hesitant Fuzzy Sets (Jun Wang, Jian-li Yu, Feng-ying Gu, Hong-Gen Chen)....Pages 287-295
Active Disturbance Rejection and Adaptive Backstepping Control for Induction Motor with Smooth Switching of Rotor Flux (Fei Gong, Haisheng Yu, Jinpeng Yu, Xudong Liu)....Pages 296-304
Super-Twisting and Nonsingular Terminal Sliding Mode Direct Torque Control of Induction Motor Drives (Wenchao Lv, Haisheng Yu)....Pages 305-312
Four Quadrant PMSM Drive System via Backstepping and Hamiltonian Control (Guanglin Lv, Haisheng Yu, Xudong Liu)....Pages 313-319
Sliding Mode Control of Induction Motor Based on AC-DC-AC Converter (Huipeng Zhang, Haisheng Yu)....Pages 320-328
Field Environment Intelligent Navigation System for Tomato Transportation Robot Based on Dijkstra (Xiaonan Guo, Yifei Chen, Jianwei Zhao, Liu Yang, Wenwen Gong)....Pages 329-336
Reducing Bullwhip Effects in Supply Chain Systems via \( \varvec{H}_{\infty } \) Control (Chen Qian, Qingwei Chen)....Pages 337-346
Visual-Inertial Localization and Map Summarization Based on Prior Map (Bo Fu, Yanmei Jiao, Xiaqing Ding, Yue Wang, Rong Xiong)....Pages 347-355
A Deep Learning Method for Heartbeat Detection in ECG Image (Zewen He, Jinghao Niu, Junhong Ren, Yajun Shi, Wensheng Zhang)....Pages 356-363
Multi-mode Design and Constant Current Control of Hydraulically Interconnected Energy-Regenerative Suspension (Ruochen Wang, Dong Sun, Renkai Ding, Xiangpeng Meng)....Pages 364-372
A Temperature Control Method for Car Room Based on Single User Personalized Comfort (Nan Ye, Lin-hua Zhuang, Ning Li)....Pages 373-381
Three-Dimensional Trajectory Optimization Design of Parafoil System Obstacle Avoidance Based on Switched System Method (Qiaodan Liu, Xiang Wu)....Pages 382-392
Design of Automatic Illumination Culture System for Haematococcus pluvialis Based on LED (Shigang Cui, Xinqi Li, Yongli Zhang, Xingli Wu, Lin He)....Pages 393-400
Study on pH Control of Haematococcus pluvialis Solution Based on Neural Network Controller (Shigang Cui, Yunqi Huang, Lin He, Yongli Zhang, Xingli Wu)....Pages 401-407
An Autoencoder-Based Dimensionality Reduction Algorithm for Intelligent Clustering of Mineral Deposit Data (Yan Li, Xiong Luo, Maojian Chen, Yueqin Zhu, Yang Gao)....Pages 408-415
The Pressure Control System for Tea Rolling Based on Fuzzy Control (Yao Li, Zhe Tang, Fang Qi, Chunwang Dong)....Pages 416-425
Design of Differential GPS System Based on BP Neural Network Error Correction for Precision Agriculture (Gangshan Wu, Chiyuan Chen, Ning Yang, Haifang Hui, Peifeng Xu)....Pages 426-438
Multi-view Based Pose Alignment Method for Person Re-identification (Yulei Zhang, Qingjie Zhao, You Li)....Pages 439-447
A Novel Contribution Graph Based Likert Scale Method and Its Application to Real-Time Alarm Evaluation (Qun-Xiong Zhu, Rui Ding, Yan-Lin He, Yuan Xu)....Pages 448-455
Research on Optimization of Intelligent Assignment of Crane Task Priority (Hexu Sun, Pengcheng Wang, Zhaoming Lei)....Pages 456-464
Optimization of Vehicle Scheduling Within the Steel Enterprises Based on IAGSO Algorithm (He-xu Sun, Fan Zhao, Zhaoming Lei)....Pages 465-472
Research on Wind Power Optimization Scheduling Based on Improved Plant Growth Simulation Algorithm (Hexu Sun, Hang Zhang, Zhaoming Lei)....Pages 473-481
An Improved BING/NMS Algorithm for Aircraft Detection (Jianxin Feng, Junmei Liu, Chengsheng Pan)....Pages 482-493
Research on Wind Power Consumption Dispatching Based on Improved Whale Optimization Algorithm (Hexu Sun, Wei Zhang, Zhaoming Lei)....Pages 494-502
Time Delay Estimation Based PD Sliding Mode Control of Hybrid Robot for Automobile Electro-Coating Conveying (Qiuyue Qin, Guoqin Gao, Shilin Lei)....Pages 503-511
A Predictive Speed Control Method Based on Sliding Mode Model for PMSM Drive System (Qian Guo, Tianhong Pan)....Pages 512-520
The Optimal Mars Entry Guidance with External Disturbance Using Neural Network Solution (Maomao Li, Ruike Guo)....Pages 521-528
Fault Tolerant Control for Five-Phase Synchronous Reluctance Motor by Third Harmonic Current Injection (Guohai Liu, Jiajun Ni, Qian Chen)....Pages 529-536
Echo State Network with Hub Property (Fanjun Li, Ying Li, Xiaohong Wang)....Pages 537-544
Robust Control of Fractional-Order Horizontal Platform System with Input Saturation (Xiaomin Tian, Zhong Yang)....Pages 545-552
IoT System Data Quality Optimization: Research Status and Problem Analysis (Haoyu Jiang, Jiacheng Ji, Quanbo Ge, Chunxi Li)....Pages 553-561
Numerical Verification and Robotic Application of New DTZD Algorithm for Solving System of Time-Varying Nonlinear Equations (Zhijing Huang, Xinjie Lin, Yiwen Zhang, Zhixin Zhang, Dongsheng Guo)....Pages 562-570
Fault Detection Based on Multi-local SVDD with Generalized Additive Kernels (Huangang Wang, Daoming Li, Junwu Zhou, Xu Wang)....Pages 571-579
A Cooperative Target 3D Tracking Method Based on EPnP and Adaptive Kalman Filter (Haodong Ding, Kun Liu, Peng Chen, Haiyong Chen)....Pages 580-591
Adaptive Sliding Mode Control for a 6 DOFs Magnetic Levitation System (Meng Duan, Yingmin Jia, Kai Gong, Yuxin Jia)....Pages 592-602
Research on the Intelligent Control System for Solar Greenhouse in Consideration of Indoor Dynamic Environment Information (Wenwen Gong, Dong Pu, Xiaonan Guo, Xiangnan Zhang, Yifei Chen)....Pages 603-610
Recursive Relaxation Algorithm for Identification of Multiple Input Multiple Output Systems (Ying Zhou, Jing-song Yang, Tong Wang, Hong Wang)....Pages 611-619
Image Restoration Based on Wavelet Semi-soft Threshold Transform and BP Fuzzy Neural Network (Wenjing Pei, Yingmin Jia)....Pages 620-628
An Iterative Parameter Tuning Method for Robot Joint Motor’s Sliding Mode Controller (Jie Li, Haibo Yu, Yanbo Wang, Bokai Xuan, Zhe Chen)....Pages 629-637
Fault Feature Extraction of Wind Turbine Rolling Bearing Based on PSO-VMD (Ping Zhang, Jingmin Yan)....Pages 638-646
Quantized Kernel Learning Filter with Maximum Mixture Correntropy Criterion (Lin Chu, Wenling Li)....Pages 647-655
Discovering Bursty Events Based on Enhanced Bursty Term Detection (Liyan Zhou, Junping Du, Wanqiu Cui, Zhe Xue, Chengcai Chen)....Pages 656-663
Dynamic Job Shop Scheduling Problem with New Job Arrivals: A Survey (Zhen Wang, Jihui Zhang, Jianfei Si)....Pages 664-671
Overview of Longitudinal and Lateral Control for Intelligent Vehicle Path Tracking (Tengfei Fu, Chenwei Yao, Mohan Long, Mingqin Gu, Zhiyuan Liu)....Pages 672-682
The Development of Web Application Front-End of Intelligent Clinic Based on Vue.js (Minghang Li, Jianghai Hu, Xianwu Lin)....Pages 683-690
Topic Detection Based on Semantics, Time and Social Relationship (Pengchao Cheng, Junping Du, Feifei Kou, Zhe Xue, Peihua Chen)....Pages 691-698
Research on Metadata System and Model of Military Logistics Information Resources (Jun Wang, DaRong Ling, Wenbing Liu, Siying Hu, Fan Jiang)....Pages 699-708
A General Technique to Combine Off-Policy Reinforcement Learning Algorithms with Satellite Attitude Control (Jian Zhang, Fengge Wu, Junsuo Zhao, Fanjiang Xu)....Pages 709-719
Finite-Time Event-Triggered Attitude Consensus Control for Multiple Unmanned Surface Vessels (Sichen Liu, Qing Fei, Changwen Wu, Xiaosong Huang)....Pages 720-727
Kinect-Based Gesture Tracking in Remote Operation of Rocket Casket (Jianxin Zhang, Xiaowang Jiang, Guolei Wang, Zhiliang Chen, Wenzhu Deng)....Pages 728-735
Water Quality Modeling and Prediction Method Based on Sparse Recurrent Neural Network (Zhenbo Cheng, Zhengyuan Shen, Tianqi Zhu, Huaidi Lin, Leilei Zhang)....Pages 736-747

Citation preview

Lecture Notes in Electrical Engineering 586

Zhidong Deng Editor

Proceedings of 2019 Chinese Intelligent Automation Conference

Lecture Notes in Electrical Engineering Volume 586

Series Editors Leopoldo Angrisani, Department of Electrical and Information Technologies Engineering, University of Napoli Federico II, Naples, Italy Marco Arteaga, Departament de Control y Robótica, Universidad Nacional Autónoma de México, Coyoacán, Mexico Bijaya Ketan Panigrahi, Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India Samarjit Chakraborty, Fakultät für Elektrotechnik und Informationstechnik, TU München, Munich, Germany Jiming Chen, Zhejiang University, Hangzhou, Zhejiang, China Shanben Chen, Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China Tan Kay Chen, Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore Rüdiger Dillmann, Humanoids and Intelligent Systems Lab, Karlsruhe Institute for Technology, Karlsruhe, Baden-Württemberg, Germany Haibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, China Gianluigi Ferrari, Università di Parma, Parma, Italy Manuel Ferre, Centre for Automation and Robotics CAR (UPM-CSIC), Universidad Politécnica de Madrid, Madrid, Spain Sandra Hirche, Department of Electrical Engineering and Information Science, Technische Universität München, Munich, Germany Faryar Jabbari, Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA Limin Jia, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Alaa Khamis, German University in Egypt El Tagamoa El Khames, New Cairo City, Egypt Torsten Kroeger, Stanford University, Stanford, CA, USA Qilian Liang, Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA Ferran Martin, Departament d’Enginyeria Electrònica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain Tan Cher Ming, College of Engineering, Nanyang Technological University, Singapore, Singapore Wolfgang Minker, Institute of Information Technology, University of Ulm, Ulm, Germany Pradeep Misra, Department of Electrical Engineering, Wright State University, Dayton, OH, USA Sebastian Möller, Quality and Usability Lab, TU Berlin, Berlin, Germany Subhas Mukhopadhyay, School of Engineering & Advanced Technology, Massey University, Palmerston North, Manawatu-Wanganui, New Zealand Cun-Zheng Ning, Electrical Engineering, Arizona State University, Tempe, AZ, USA Toyoaki Nishida, Graduate School of Informatics, Kyoto University, Kyoto, Japan Federica Pascucci, Dipartimento di Ingegneria, Università degli Studi “Roma Tre”, Rome, Italy Yong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore Joachim Speidel, Institute of Telecommunications, Universität Stuttgart, Stuttgart, Baden-Württemberg, Germany Germano Veiga, Campus da FEUP, INESC Porto, Porto, Portugal Haitao Wu, Academy of Opto-electronics, Chinese Academy of Sciences, Beijing, China Junjie James Zhang, Charlotte, NC, USA

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Zhidong Deng Editor

Proceedings of 2019 Chinese Intelligent Automation Conference

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Editor Zhidong Deng Tsinghua University Beijing, China

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-32-9049-5 ISBN 978-981-32-9050-1 (eBook) https://doi.org/10.1007/978-981-32-9050-1 © Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Contents

Research on Combat Deduction Platform Technology for Intelligent Operational Decision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xin Liao and Zheng-hao Sun

1

Study of the Auxiliary Robot Used to Disassemb and Assemb Mid-Set Switch Cubicle Based on BCI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weiwei Huang, Bihui Zhang, and Rui Li

14

Ship Detection Based on Faster R-CNN Network in Optical Remote Sensing Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Min Zhai, Huaping Liu, Fuchun Sun, and Yan Zhang

22

An Efficient Real-Time Indoor Autonomous Navigation and Path Planning System for Drones Based on RGB-D Sensor . . . . . . Ran Xiao, Hao Du, Chaowen Xu, and Wei Wang

32

Research on Port Throughput Prediction of Tianjin Port Based on PCA-SVR in the New Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jinyu Wei, Yuqiao Tang, Yang Yu, and Xueshan Sun

45

Research on Automatic Detection Method of Part Shape Based on Projection Optics and Image Processing . . . . . . . . . . . . . . . . . . . . . . Xiangyang Sun, Binggao He, Can Wang, and Yuegang Fu

53

Signal Detection, Processing and Challenges of Non-invasive Brain-Computer Interface Technology . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoyuan Li, Feng Chen, Yaohui Jia, and Xinyu Liu

60

An Iterative Model from Grid Cells to Place Cells . . . . . . . . . . . . . . . . . Naigong Yu, Yishen Liao, Xiangguo Zheng, and Hui Feng Visual Stabilization of Wheeled Mobile Robots with Unknown Visual Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yao Huang and Jianbo Su

68

79

v

vi

Contents

A Game Model for Gomoku Based on Deep Learning and Monte Carlo Tree Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiali Li, Shuai He, Licheng Wu, Daiyao Chen, and Yue Zhao

88

Cascading Failure Analysis of Military Command and Control Network Based on SIS Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jichao Xing, Zhaoliang Zhu, Chuxiang Chen, and Xiaofeng Guo

98

Laser Scan Matching in Polar Coordinates Using Gaussian Process . . . 106 Yinqiang Wang, Bo Li, Bo Han, Yu Zhang, and Wenjie Zhao An Evolutionary Membrane Algorithm Based on Competition Mechanism for Multi-objective Optimization Problems . . . . . . . . . . . . . 116 Zhiqiang Geng, Yunfei Cui, and Yongming Han Design of Intelligent Measuring Device for On-line Machining Parts of Lathe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 Xiangyang Sun, Binggao He, Lijuan Shi, Can Wang, and Yuegang Fu Algorithm Research on Optimizing Ordering and Pricing Policy for Perishable Items . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Xin Yang, Yuan Zhao, Jin-yu Wei, and Yang Yu Facial Expression Recognition System Based on Deep Residual Fusion Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 Haonan Wang, Junhang Ding, Fan Wang, and Zhe Ma Research and Application on Ensemble Learning Methods . . . . . . . . . . 145 Yuzhong Wang Optimal Fault Tolerant Control of Five-Phase Permanent Magnet Synchronous Motor Under One-Phase Open-Circuit Fault . . . . . . . . . . 156 Song Jie Enhanced Pulse Density Modulation for Efficiency Optimization in Inductive Power Transfer Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 Hong Zheng, Rui Bian, and Yubing Gu RGB-D Saliency Object Detection Based on Adaptive Manifolds Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 Lingling Zi, Xin Cong, Yanfei Peng, and Xitao Chen Study of Speed Stabilization Loop for Airborne Photoelectric Platform Based on Active Disturbance Rejection Control . . . . . . . . . . . . . . . . . . . 182 Yongli Bi and Shigang Wang Active Disturbance Rejection Control of Drum Water Level with Generalized Extended State Observer . . . . . . . . . . . . . . . . . . . . . . 191 Cuiping Pu, Jie Ren, and Jianbo Su

Contents

vii

Improved Cuckoo Search Algorithm Based on Exponential Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 Kun Wang, Xiaofeng Lian, and Bing Pan Autonomous Intelligent Control for Path Following of Unmanned Surface Vessels with Input Constraints . . . . . . . . . . . . . . . . . . . . . . . . . 208 Yalei Yu, Chen Guo, and Haomiao Yu Investigation on Energy Feedback Potentiality of New Hydraulic Interconnected Energy-Regenerative Suspension . . . . . . . . . . . . . . . . . . 216 Zeyu Sun, Ruochen Wang, Xiangpeng Meng, and Qiuiming Jiang Sliding Mode Control with Uncertain Model for a Quadrotor UAV’s Automatic Visual Landing Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226 Qing Fei, Jiaxiang Zhang, Zhengyang Wang, and Xiaosong Huang Depth-Fusion Based on Gaussian Mixture Model for RGB-D Visual SLAM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234 Zhaotong Ding, Ran Huang, and Biao Hu Error Analysis of Dual Antenna UAV Tracking System . . . . . . . . . . . . 243 Shujuan Li, Junhang Ding, and Jianzhi Li Fault Tolerant Control Allocation Based on Adaptive Sliding Mode Control for Distributed-Driven Electric Vehicle . . . . . . . . . . . . . . . . . . . 251 Guohai Liu, Shuangjian Wang, Duo Zhang, Yue Shen, and Zhen Yao Research on Sliding Mode Active Disturbance Rejection Control and Thrust Allocation of Dynamic Positioning System . . . . . . . . . . . . . . 262 Zaiji Piao and Chen Guo Research on Indoor Positioning Method Based on Improved HS-AlexNet Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270 Libiao Zhang, Rui Zhao, Yuqing Liu, Xinyu Yang, and Shipeng Li A Modified Energy and Signal Coordination Control Strategy for a Robotic System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278 Yu Wang, Haisheng Yu, Jinpeng Yu, Herong Wu, and Xudong liu Parameter Optimization Control of Multiple Response Process Based on Hesitant Fuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 Jun Wang, Jian-li Yu, Feng-ying Gu, and Hong-Gen Chen Active Disturbance Rejection and Adaptive Backstepping Control for Induction Motor with Smooth Switching of Rotor Flux . . . . . . . . . . 296 Fei Gong, Haisheng Yu, Jinpeng Yu, and Xudong Liu Super-Twisting and Nonsingular Terminal Sliding Mode Direct Torque Control of Induction Motor Drives . . . . . . . . . . . . . . . . . . . . . . 305 Wenchao Lv and Haisheng Yu

viii

Contents

Four Quadrant PMSM Drive System via Backstepping and Hamiltonian Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313 Guanglin Lv, Haisheng Yu, and Xudong Liu Sliding Mode Control of Induction Motor Based on AC-DC-AC Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320 Huipeng Zhang and Haisheng Yu Field Environment Intelligent Navigation System for Tomato Transportation Robot Based on Dijkstra . . . . . . . . . . . . . . . . . . . . . . . . 329 Xiaonan Guo, Yifei Chen, Jianwei Zhao, Liu Yang, and Wenwen Gong Reducing Bullwhip Effects in Supply Chain Systems via H 1 Control . . . . Chen Qian and Qingwei Chen

337

Visual-Inertial Localization and Map Summarization Based on Prior Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 Bo Fu, Yanmei Jiao, Xiaqing Ding, Yue Wang, and Rong Xiong A Deep Learning Method for Heartbeat Detection in ECG Image . . . . . 356 Zewen He, Jinghao Niu, Junhong Ren, Yajun Shi, and Wensheng Zhang Multi-mode Design and Constant Current Control of Hydraulically Interconnected Energy-Regenerative Suspension . . . . . . . . . . . . . . . . . . 364 Ruochen Wang, Dong Sun, Renkai Ding, and Xiangpeng Meng A Temperature Control Method for Car Room Based on Single User Personalized Comfort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 Nan Ye, Lin-hua Zhuang, and Ning Li Three-Dimensional Trajectory Optimization Design of Parafoil System Obstacle Avoidance Based on Switched System Method . . . . . . 382 Qiaodan Liu and Xiang Wu Design of Automatic Illumination Culture System for Haematococcus pluvialis Based on LED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 Shigang Cui, Xinqi Li, Yongli Zhang, Xingli Wu, and Lin He Study on pH Control of Haematococcus pluvialis Solution Based on Neural Network Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 Shigang Cui, Yunqi Huang, Lin He, Yongli Zhang, and Xingli Wu An Autoencoder-Based Dimensionality Reduction Algorithm for Intelligent Clustering of Mineral Deposit Data . . . . . . . . . . . . . . . . . 408 Yan Li, Xiong Luo, Maojian Chen, Yueqin Zhu, and Yang Gao The Pressure Control System for Tea Rolling Based on Fuzzy Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416 Yao Li, Zhe Tang, Fang Qi, and Chunwang Dong

Contents

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Design of Differential GPS System Based on BP Neural Network Error Correction for Precision Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . 426 Gangshan Wu, Chiyuan Chen, Ning Yang, Haifang Hui, and Peifeng Xu Multi-view Based Pose Alignment Method for Person Re-identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439 Yulei Zhang, Qingjie Zhao, and You Li A Novel Contribution Graph Based Likert Scale Method and Its Application to Real-Time Alarm Evaluation . . . . . . . . . . . . . . . 448 Qun-Xiong Zhu, Rui Ding, Yan-Lin He, and Yuan Xu Research on Optimization of Intelligent Assignment of Crane Task Priority . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456 Hexu Sun, Pengcheng Wang, and Zhaoming Lei Optimization of Vehicle Scheduling Within the Steel Enterprises Based on IAGSO Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465 He-xu Sun, Fan Zhao, and Zhaoming Lei Research on Wind Power Optimization Scheduling Based on Improved Plant Growth Simulation Algorithm . . . . . . . . . . . . 473 Hexu Sun, Hang Zhang, and Zhaoming Lei An Improved BING/NMS Algorithm for Aircraft Detection . . . . . . . . . 482 Jianxin Feng, Junmei Liu, and Chengsheng Pan Research on Wind Power Consumption Dispatching Based on Improved Whale Optimization Algorithm . . . . . . . . . . . . . . . . 494 Hexu Sun, Wei Zhang, and Zhaoming Lei Time Delay Estimation Based PD Sliding Mode Control of Hybrid Robot for Automobile Electro-Coating Conveying . . . . . . . . . . . . . . . . . 503 Qiuyue Qin, Guoqin Gao, and Shilin Lei A Predictive Speed Control Method Based on Sliding Mode Model for PMSM Drive System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 512 Qian Guo and Tianhong Pan The Optimal Mars Entry Guidance with External Disturbance Using Neural Network Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 521 Maomao Li and Ruike Guo Fault Tolerant Control for Five-Phase Synchronous Reluctance Motor by Third Harmonic Current Injection . . . . . . . . . . . . . . . . . . . . . . . . . . 529 Guohai Liu, Jiajun Ni, and Qian Chen Echo State Network with Hub Property . . . . . . . . . . . . . . . . . . . . . . . . . 537 Fanjun Li, Ying Li, and Xiaohong Wang

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Robust Control of Fractional-Order Horizontal Platform System with Input Saturation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 545 Xiaomin Tian and Zhong Yang IoT System Data Quality Optimization: Research Status and Problem Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 553 Haoyu Jiang, Jiacheng Ji, Quanbo Ge, and Chunxi Li Numerical Verification and Robotic Application of New DTZD Algorithm for Solving System of Time-Varying Nonlinear Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 562 Zhijing Huang, Xinjie Lin, Yiwen Zhang, Zhixin Zhang, and Dongsheng Guo Fault Detection Based on Multi-local SVDD with Generalized Additive Kernels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 571 Huangang Wang, Daoming Li, Junwu Zhou, and Xu Wang A Cooperative Target 3D Tracking Method Based on EPnP and Adaptive Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 580 Haodong Ding, Kun Liu, Peng Chen, and Haiyong Chen Adaptive Sliding Mode Control for a 6 DOFs Magnetic Levitation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 592 Meng Duan, Yingmin Jia, Kai Gong, and Yuxin Jia Research on the Intelligent Control System for Solar Greenhouse in Consideration of Indoor Dynamic Environment Information . . . . . . . 603 Wenwen Gong, Dong Pu, Xiaonan Guo, Xiangnan Zhang, and Yifei Chen Recursive Relaxation Algorithm for Identification of Multiple Input Multiple Output Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 611 Ying Zhou, Jing-song Yang, Tong Wang, and Hong Wang Image Restoration Based on Wavelet Semi-soft Threshold Transform and BP Fuzzy Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 620 Wenjing Pei and Yingmin Jia An Iterative Parameter Tuning Method for Robot Joint Motor’s Sliding Mode Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 629 Jie Li, Haibo Yu, Yanbo Wang, Bokai Xuan, and Zhe Chen Fault Feature Extraction of Wind Turbine Rolling Bearing Based on PSO-VMD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 638 Ping Zhang and Jingmin Yan Quantized Kernel Learning Filter with Maximum Mixture Correntropy Criterion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 647 Lin Chu and Wenling Li

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Discovering Bursty Events Based on Enhanced Bursty Term Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656 Liyan Zhou, Junping Du, Wanqiu Cui, Zhe Xue, and Chengcai Chen Dynamic Job Shop Scheduling Problem with New Job Arrivals: A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 664 Zhen Wang, Jihui Zhang, and Jianfei Si Overview of Longitudinal and Lateral Control for Intelligent Vehicle Path Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 672 Tengfei Fu, Chenwei Yao, Mohan Long, Mingqin Gu, and Zhiyuan Liu The Development of Web Application Front-End of Intelligent Clinic Based on Vue.js . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683 Minghang Li, Jianghai Hu, and Xianwu Lin Topic Detection Based on Semantics, Time and Social Relationship . . . 691 Pengchao Cheng, Junping Du, Feifei Kou, Zhe Xue, and Peihua Chen Research on Metadata System and Model of Military Logistics Information Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 699 Jun Wang, DaRong Ling, Wenbing Liu, Siying Hu, and Fan Jiang A General Technique to Combine Off-Policy Reinforcement Learning Algorithms with Satellite Attitude Control . . . . . . . . . . . . . . . . . . . . . . . 709 Jian Zhang, Fengge Wu, Junsuo Zhao, and Fanjiang Xu Finite-Time Event-Triggered Attitude Consensus Control for Multiple Unmanned Surface Vessels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 720 Sichen Liu, Qing Fei, Changwen Wu, and Xiaosong Huang Kinect-Based Gesture Tracking in Remote Operation of Rocket Casket . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 728 Jianxin Zhang, Xiaowang Jiang, Guolei Wang, Zhiliang Chen, and Wenzhu Deng Water Quality Modeling and Prediction Method Based on Sparse Recurrent Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 736 Zhenbo Cheng, Zhengyuan Shen, Tianqi Zhu, Huaidi Lin, and Leilei Zhang

Research on Combat Deduction Platform Technology for Intelligent Operational Decision Xin Liao1,2(&)

and Zheng-hao Sun1,2

1

2

Aerospace System Development Research Center, China Aerospace Science and Technology Corporation, Beijing 100094, China [email protected] R&D Center, China Academy of Launch Vehicle Technology, Beijing 100076, China

Abstract. Artificial intelligence technology is expected to become a powerful force to promote the evolution of war patterns in the future. AI technology has the ability to use data fusion for rapid decision-making, and will become a key supporting technology for operational command. This paper analyzes the relation between military and intelligent decision-making, analyzes the structural characteristics of combat problems according to the requirements of command decision-making learning and training of artificial intelligence, proposes a design idea of antagonistic deduction platform, and explains the key problems in its architecture design, as well as the data exchange method between platform and intelligent learning methods. Finally, the system implementation and technical verification are carried out in a small area denial scenario. The platform supports the access to intelligent command program based on autonomous game decision net, can run real-time intelligent confrontation under asymmetric information, and provides intelligent opponent and benchmark for combat command training. Keywords: Artificial intelligence Combat deduction


Assistant decision-making


1 Introduction The development and application of intelligent technology has brought ceaseless changes to the military field. As a groundbreaking technology in the development pattern of science and technology, if applied in the battlefield, AI technology will become a multiplier of battlefield intelligent perception and information processing. It will greatly enhance the capability of a specific link or a single technology in the battle process, leading to the change of the overall war form. Nowadays, the new equipment and new combat concepts such as unmanned military platform, bionic robot, soldier’s physical fitness/intelligent assistant equipment, intelligent command and control assistant decision-making and so on in the fields of intelligent perception and information processing are emerging in endlessly. Among © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 1–13, 2020. https://doi.org/10.1007/978-981-32-9050-1_1

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them, developing battlefield situation understanding and rapid decision-making ability based on multidimensional information, realizing battlefield integrated perception and the intelligence of decision-making, and applying it to command and control intelligent assistant decision-making or pre-war global confrontation deduction required in future warfare, has been one of the research hot-spots in the field of military intelligence [1]. Intelligent algorithms, application scenarios and data are the three main elements of the application of intelligent technology. In order to introduce the reinforcement learning method into the decision-making of combat command, it is necessary to analyze and extract the application scenarios, that is, the combat mode, and then visualize, and establish a combat simulation environment that can simulate the realtime confrontation of all parties in the operation, so as to provide effective training data, learning environment and verification method for the deep reinforcement learning method. This paper focuses on how to construct the deduction platform of combat confrontation supporting AI, and analyzes the key problems of military intelligent decision-making, analyzes the wide-area denial combat scenarios, proposes an integrated technical framework of combat decision-making training and demonstration for machine learning needs, integrates military models such as sea, air and rocket under limited scenarios, and defines the overall combat scenario and equipment rules. It supports equipment operating strategy confrontation under uncertain and incomplete information conditions, forms an effective training data source of intelligent algorithm, completes the early part of technical verification of operational command intelligence, and lay a technical foundation for operational decision-making of high dynamic confrontation including multi-services and arms under simulated real scenarios.

2 Military and Intelligent Decision Making One of the starting points of AI is to optimize decision-making, and then achieve the best result, which is the same problem faced by many other fields, including military command and operations. In recent years, a series of new research achievements, such as deep reinforcement learning technology and Multi-Agent Reinforcement Learning method, have brought opportunities to the intelligence of operational command information system. AI go player AlphaGo [2], AI assistants for fighter pilots ALPHA [3], DeepStack [4], Libratus [5] and other AI programs, in chess, flight, card games, Star-Craft and other confrontational scenarios to challenge human players, trying to collect data from their own side and the other side within a limited time, analyze the current situation and predict the other side’s actions in real time, and then make the correct response. Some strategy controllers work unexpectedly and even surpass human beings in some cases. In some cases, it is necessary to consider how to make reasoning and planning under incomplete information, and even to use multi-agent cooperation in complex tasks to measure the short-term/long-term benefit balance. This kind of game can actually be seen as a partial simplification of the real battlefield. The implementation

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mode of AI controller, including multi-agent learning architecture, can provide effective reference for intelligent decision-making in combat scenarios from the following two aspects. Firstly, in the traditional battle command mode, the understanding of the integrated battlefield situation largely depends on the commander’s intuitive judgment. When confronted with incomplete information and too complex weaponry, it is difficult for commanders to conduct effective command of system-of-systems confrontation operations. Only a small number of expert commanders who have been engaged in equipment development and command training for a long time can make relatively good judgments. Compared with manual information analysis, AI has great advantages in massive data search, storage, calculation and mining. Using Value Network algorithm similar to AlphaGo to judge the situation of chess and the probability of winning or losing can provide data support for commanders to understand the battlefield situation, help commanders to achieve rapid decision-making, and gain strategic priority in battle command. Secondly, the traditional auxiliary methods of war situation deduction are limited by the prior knowledge of the participants and the unclear use of equipment, which has limited assistant decision-making role for commanders. With the development of indepth learning methods and deepening research, AI technology has gradually changed from sample learning mode based on massive data to self-learning evolution mode in small sample or no sample environment. Algorithms such as deep reinforcement learning, multi-agent learning and migration learning can improve the command ability of intelligent assistants and provide assistant support for commanders decision-making [6]. Of course, because the complexity of war is much more than the regular games, AI has a long way to go if we want to truly realize the simulation and decision support of war [7].

3 Analysis and Modeling of Operational Problems Operational process generally includes “detection, control, judgment, fight and evaluation” processes, involving “information chain”, “command chain”, “strike chain” and multiple links collaboration. It requires that under the support of interconnected information network, multiple parts seamless connection, efficient collaboration, forming a closed loop, realizing the information flow to tract material flow, and win operational advantage with information advantage. In order to achieve effective simulation of combat confrontation process, this paper simplifies and abstracts the combat scenario, and constructs a complete multidimensional combat process link by using weapon equipment platform and capability model, combat rules model based on expert system and combat deduction environment model, as shown in Fig. 1.

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Fig. 1. Model combination diagram

3.1

Weapon Equipment Platform and Capability Model

The model system of combat deduction platform research should cover all types of weapons and equipment involved in combat scenario. At the same time, the equipment behavior of both sides should be analyzed in combination with the respective decisionmaking modes of the two sides, and the model system and behavior response suitable for both sides operation should be constructed. The model system is divided into red-square model set and blue-square model set according to combat. In the current scenario, the model set is responsible for the capability, behavior and response of land, sea, air and space equipment, including the capabilities of damage, detection, maneuvering, penetration, communication and information confrontation. Red and blue are back-to-back confrontations, and their perceptions are limited. They can only get the situation information of each other through the detection and monitoring means. 3.2

Operational Rule Model Based on Expert System

The operational rule model is equivalent to the director/referee role in the operational confrontation deduction platform. Based on the weapon equipment platform and capability model, the basic operational interaction rules of the red and blue sides in their respective operational objectives and confrontation are stipulated, and the initial operational resources are allocated. In the process of deduction, expert system is used to make decision, and the rules of confrontation arbitration between red and blue parties are given. The operational rules model defines the constraint rules from the aspects of battlefield overall situation, operational interaction and system deduction, including interception rules, missile launching rules, equipment deployment rules, deduction event triggering rules, communication rules, environment constraint rules, mission rules and mission evaluation rules. 3.3

Battle Deduction Environment Model

Environmental model is the basic model in operational deduction, including morningdusk model (solar model), meteorological environment model and geographic

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environment model (terrain, road network, sea state, navigation channel). Through the definition and management of these environments, environmental impact factors are provided for weapon equipment model, and the impact of different battlefield environment research on operational process can be analyzed.

4 Architecture Design of Battle Deduction Platform Supporting AI Aiming at the requirements of intelligent command training and application, the promotion mechanism and application environment technology suitable for multi-agent joint operation are studied. A scalable, flexible and fast human-computer interaction platform is constructed to realize the functions of training process management, confrontation process promotion, model management, event and message management, data recording and acquisition, human-computer interaction interface and so on. The battle deduction platform supporting AI needs to be deployed from four levels: hardware platform, system layer, management service layer and application layer. The hardware platform provides AI-oriented architecture design, using GPU and FGPA to provide fast training and reasoning computing resources; the system layer manages, dispatches and monitors computing resources for complex computing environment, deploys management infrastructure system to achieve effective utilization of the system; and the management service layer aims at the core of operational countermeasures data training and verification business on the basis of the system layer. It encapsulates general functions in the form of modules, which are provided to the application layer for invocation. The application layer mainly provides direct display interface for users, and designs deep learning model and algorithm for practical application requirements. The four-tier structure is shown in the following Fig. 2.

Fig. 2. Platform overall hierarchy division

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Combat deduction platform provides three modes: man-machine confrontation, man-to-man confrontation and machine confrontation. It integrates the data of personnel participating in confrontation and self-learning data, updates the training model by using the evaluated data, and provides self-labeling, aggregation and adjustment of training data. Compared with the traditional confrontation simulation system, the deduction platform studied in this paper improves the process propulsion technology, battlefield situation data interface and situation display mode aimed at the requirements of intelligent technology. 4.1

Propulsion Engine Based on Intelligent Rules

In order to meet the needs of data training and learning, the combat deduction platform needs to meet the needs of both the speediness of process advancement and the embedded operation of operational decision. On the one hand, it is necessary to control a large amount of time-consuming weapon equipment action time and quickly accumulate the results of confrontation as training data. On the other hand, it is necessary to leave operating space for the action instructions given by the strategy controller. Therefore, multiple modes and variable step-size propulsion mechanism is adopted to support multi-trigger mode to meet the real-time acceleration and deceleration needs of both sides in the course of confrontation. With the unified management of simulation time, the functions of simulation time advance, pause and acceleration are realized, and the simulation time with different synchronization is unified to drive the simulation process and synchronize the model. At the same time, the current simulation time is pushed to both sides of the confrontation and each situation display side. In the system design, the function of counteracting real-time interference is added. In the time step, the adjusted rule map is integrated with the current combat situation to realize the control of combat confrontation. Based on operational model, rules of engagement and basic data, the adjustment of operational process and command and control activities is completed. The contents of adjustment include troop deployment, force formation, equipment configuration, task allocation plan, operation time and steps, etc. Adding model run triggers to the system, adding the definition interface of state, time and event triggers, and providing the definition of the relationship between various triggers and combat phases, as shown in Fig. 3.

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Fig. 3. Process propulsion diagram

4.2

Intelligent Tactical Decision and Situation Awareness Interface Technology

In combat confrontation, AI agents or people are required to give tactical decisionmaking actions according to the change of battlefield situation, including equipment configuration, supply, attack, penetration and defense actions. The battlefield situation awareness interface involves the description of environmental uncertainty, the expression of multidimensional and massive situation information, and the comprehensive quantitative analysis technology of threat situation. In order to improve the accuracy of situation awareness and provide more abundant local information, battlefield situation includes not only the information of the current confrontation space state, but also the information of the historical process of each past round. Therefore, battlefield situation awareness is not only a problem of modeling and transferring complex static data (data at a time in confrontation space), but also a problem of modeling dynamic sequential data. 4.3

Multidimensional Confrontation Situation and Evolution Display of AI Learning

In the process of AI learning evolution, we need to understand the probability distribution of all possible deployment and utilization of the opponent’s military resources, and update the probability distribution as the game progresses. Therefore, it is necessary to integrate the learning results of each stage of intelligent evolution and display the key information intuitively through the interface. 2D and 3D perspectives can show the different focus of the combat process. Using the characteristics of 2D map, which is easy to display and understand, we focus on the battlefield situation, and show the global and local situation of the battlefield by color, block, data and chart. The use of 3D maps is conducive to display the characteristics of space confrontation, focusing on the global battlefield environment, space confrontation situation, while switching to

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close-range camera to focus on the impact factors and evaluation results of single, single and local objects.

5 Learning Framework of Confrontation Strategies In the intelligence of combat decision-making, it is necessary to select appropriate intelligent learning methods to test and find the best model of battlefield situation understanding and trend prediction. Deep reinforcement learning combines the perception ability of deep learning with the decision-making ability of reinforcement learning. It can continuously carry out end-to-end learning through simulation and deduction, and realize direct control from original input to output. Combining with combat confrontation deduction platform, it can effectively realize combat assistant decision-making ability. Deep learning combines the characteristics of battlefield situation and decision-making factors by using the multi-layer network structure and the non-linear transformation between the multi-layers to form an abstract and more accurate high-level representation, which has a better effect on battlefield situation perception, analysis and understanding. The cumulative reward value gained by reinforcement learning through deduction and operation, which constantly revises the network, is very suitable for solving the problem of limited amount of data such as combat assistant decision-making. Combining deep learning with reinforcement learning, the learning data are generated by antagonistic deduction platform, and the circulation path of information flow and decision-making flow is established. The learning process of operating strategy is shown in Fig. 4. The state information in the perception domain is acquired by the combat deduction platform, and then the perception information is extracted and analyzed, and integrated into the battlefield state. The decision network inputs decision space and battlefield state, and outputs optimal decision. The network evaluates the quality of the actions selected by the decision-making network, and generates prediction differences to guide the updating of the network.

Fig. 4. Schematic diagram of learning framework for confrontation strategies

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In the process of continuous reinforcement learning, the action reward given by battlefield situation determines the quality of action. In order to win the final battle decision-making, it is necessary to evaluate every strategic node in the battlefield, that is, the action space in the current state, until the best action decision-making sequence is found.

6 Verification Examples Taking the deduction platform as the basic framework and the local area of the East China Sea as the confrontation scenario, the unit models of anti-ship missile, launcher, satellite, destroyer and cruise missile are constructed, and the confrontation strategy learning and verification of small-scale missile combat cluster against warship formation battle group based on RESNET network are realized, including time-sensitive target and missile attack and defense under incomplete information. There are two kinds of reconnaissance-strike-evaluation loops in war. In a given scenario, the AI commander’s ultimate success rate exceeds the human average by 15%. In this example, the command decision process is simplified, the information transmission system is omitted, and the command decision process is simulated by AI command system outside the platform. The simulation of attack process, defense process and sensor detection process are considered in the process of warfare confrontation. Design details such as the capability unit model are as indicated below. 6.1

Capability Unit Model

The application background of the validation example is the denial battle in the smallscale coastal battlefield, so the red and blue weapon systems only consider the movable platform, including multiple anti-ship missile, launcher, satellite, destroyer and cruise missile. The red side considers anti-ship missiles, launch vehicles and satellites. The blue side considers satellites, destroyers and cruise bombs. The action decision space of the unit model involved in the example is shown in Fig. 5. Missile Model. The missile model includes anti-ship missile and cruise missile. It has the ability of target terminal detection, terminal guidance, mid-range maneuver and damage. The key type parameters of missile model include name, type, range, speed, launch preparation time, cruise altitude and terminal guidance distance. Missile operation includes launching, medium range maneuver and explosion. Satellite Model. The satellite model is responsible for the operation of the satellite platform, and its key type parameters include orbit parameters, load and orbit time. Missile Launcher Model. The missile launcher model is responsible for concealing the speed, erection, withdrawal, reloading, concealment and withdrawal of the missile launcher. The operation of missile launcher includes single movement, state operation, formation movement and salvo.

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Fig. 5. Action decision space of unit model

Ship Model. The ship model is responsible for the navigation of the fleet. Ship operation includes attack missile launching, interceptor missile launching, shipborne radar switch, formation movement and salvo launching. Base Model. Each base model includes multiple positions, which are connected by road network and responsible for the operation and transfer of missile launchers. Damage Capability Model. The damage capability model is responsible for managing the warhead characteristics on the red and blue attack bombs. The key parameters include the type of damage target, damage capability and damage range.

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Defense Capability Model. The defense capability model is responsible for managing the defense characteristics on the red base. Its key parameters include defensive target type, defensive capability and defensive scope. Detection Capability Model. The detection capability model includes three kinds of payload: infrared loads on satellites, electronic reconnaissance loads and radar detection loads on ships. Its key parameters include load type, detection target type, detection range and so on. 6.2

Model of Operational Rules

The platform implements regular statistical item definition, which supports users to customize statistical items that need statistics and analysis, and supports more flexible evaluation of system against deduction. Equipment Behavior Rules. The behavior rules are for a single type of equipment. For different types of equipment, a number of groups of behavior rules are established. For example, for missile model, there are rules such as missile strike range limitation and target type limitation. Rules of Conduct. Behavior rules include rules that equipment need to follow when equipment is maneuvering, damage confrontation and detection confrontation. Rules of Termination. The rules of termination are responsible for determining whether the red and blue sides have reached the winning or losing termination conditions at each step. The factors that can be considered include the battle damage situation, the firepower survival situation, the battle process of both sides and so on. 6.3

Confrontation Environment Model

According to the background and requirement of AI confrontation, the definition and realization of geographical environment, morning and evening environment and road network in local battlefield are completed. 6.4

Intelligent Tactical Decision and Situation Awareness Interface

Aiming at the basic process of AI learning, the decision-making and perception interface between man-machine confrontation platform and AI model is designed to support training and confrontation simulation of missile, satellite, destroyer and other models, and the exchange mechanism between man-machine interface port and AI interaction port is also supported. Among them, the situation feedback interface realizes the initialization data interface including base, vehicle, missile and other models, as well as the deduced state data interface of vehicle, missile, ship, victory or defeat results, which can be used in step-by-step confrontation. According to the basic functional requirements, the control interface realizes the vehicle, ship movement command, launch vehicle release command and missile launch command. The typical interface sketch of the confrontation platform is shown in Figs. 6, 7 and 8.

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Fig. 6. Diagram of ship detection capability

Fig. 7. Diagram of satellite overhead detection capability

Fig. 8. Diagram of missile strike scenario

7 Conclusions Future warfare will show the characteristics of multidimensional battlefield, complex weapon use and diversified forms of confrontation, which will pose a high challenge to the wartime situation analysis, assessment and mission decision-making of our commanders. By building the platform of human-machine intelligent confrontation and deduction supporting AI, this paper explores the technological ways to realize the ability of self-evaluation of multidimensional complex situation and the ability of self-

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assisted decision-making based on AI, which can provide high-level opponents and benchmarks for the commander system’s operational command training, situation analysis, scheme generation and assistant decision-making support for the system’s operational command. The development of war command and confrontation control is of great significance.

References 1. Hu X, Guo S, He X (2016) Intelligent challenge of command information system - “deep green” plan and enlightenment and reflection from AlphaGo. Command Inf Syst Technol 7 (3):1–7 2. Hu X, He X, Tao J (2017) AlphaGo’s breakthrough and the challenge of chess deduction. Sci Technol Rep 35(21):49–60 3. Ma S (2017) AI pilots win over human pilots. Aviat World 44–47 4. Wang L (2017) Technical analysis of deep stack artificial intelligence system defeating texas poker professionals. Defense Perspect 64 5. Zhang X (2017) Hot spots in artificial intelligence in 2017. Big Data Era 6:69–71 6. Jin X (2018) Research on decomposition of intelligent command and control problems. J Command Control 3:64–68 7. Zhu F, Hu X, Wu L, He X, Lu X, Liao Y (2018) From situation cognition to situation intelligence cognition. J Syst Simul 3:761–771

Study of the Auxiliary Robot Used to Disassemb and Assemb Mid-Set Switch Cubicle Based on BCI Weiwei Huang(&), Bihui Zhang, and Rui Li Zhaotong Power Supply Bureau of Yunnan Power Grid Co., Ltd., Zhaotong 657000, Yunnan, China [email protected]

Abstract. In order to solve the problem that the working space of the central switchgear is limited or the current transformer is limited, and the single person cannot complete the operation, the brain-computer interface (BCI) technology is applied to the actual operation. In this paper, the d (0–4 Hz) and h(4–8 Hz) subbands of EEG signals are obtained by wavelet decomposition (WD). The relationship between the 14 pairs of EEG channels was determined using the Pearson correlation coefficient. The motion characteristics are then analyzed using the validity of brain network parameters. At the same time, eye movement features are extracted from the F3 and F4 channels. Finally, the movement characteristics identified by the brain network and eye movement features can aid in the disassembly and assembly of the transformer. The experimental results show that the accuracy of left and right motion recognition is over 97%. Compared with the traditional disassembly method, the efficiency has increased by nearly 60%. Keywords: Mid-set switch cubicle
Current transformer Disassemb and assemb
BCI
Brain networks




1 Introduction Research shows that the central switchgear is a kind of power equipment widely used in the power industry. However, when we maintain a central switchgear, it is difficult to disassemble or assemble the current transformer. The traditional operation requires two people to work together. One person lifts a current transformer with a weight of 30 kg in the middle switch cabinet with limited working space, and the other person disassembles or assembles the current transformer. However, this mode of operation is inefficient. It is easy to cause potential safety hazards of current transformer sliding, breaking or falling injury [1]. In practical application, due to the interference of environmental vibration factors in power plant, the originally aligned screw holes often slide left or right, which causes the bolt to become to be jammed (Fig. 1). Therefore, we need to develop an auxiliary device that can perform appropriate left and right fine adjustment of the current transformer position to meet the needs of transformer disassembly. © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 14–21, 2020. https://doi.org/10.1007/978-981-32-9050-1_2

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The BCI technology is a method of direct communication between the brain and the controlled device using signal acquisition and processing equipment [2]. This technology makes it possible to use the human brain to directly control external devices to help humans complete related operations [3–5]. Previous studies have shown that modern complex network theory is widely used to simulate brain function [6–8]. Combined with the characteristics of the EOG signal, the BCI technology is applied to the control of the left and right motion of the auxiliary robot. This method can ensure that the screw holes on the current transformer are aligned with the screw holes on the fixing plate of the middle switch cabinet, thereby smoothly completing the bolt removal operation. The experimental results show that the method adopts BCI technology, and only one person can complete the operation, which facilitates the disassembly or assembly operation of the current transformer.

Fig. 1. The operation of disassembly or assembly current transformer in mid-set switch cubicle

2 Algorithm 2.1

The Characteristics Analysis of Eye Movement

When one eye moves, an important EOG signal appears in the frontal lobe of the brain, which is the active area of the eye signal. This study used the channels AF3 and AF4 to study the characteristics of eye movement. It is worth noting that the waveform in Fig. 2 is symmetrical when the eye is moving. Figure 2 shows the EOG signal for monocular motion extracted from channels AF3 and AF4. Then the relationship between AF3 and AF4 was analyzed by using Pearson correlation coefficient. The Pearson correlation coefficient is expressed as: rXY ¼

E½ðX  EðXÞÞðY  EðYÞÞ EðXYÞ  EðXÞEðYÞ ¼ rX rY rX rY

ð1Þ

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Fig. 2. Eye movement signals

Where E(
) is the expectation operator, and a sequence consisting of n data samples needs to be calculated. Correspondingly, the correlation coefficient is calculated as follows:

rXY

n P xi yi  nxy n X 1 xi  x yi  y i¼1 ¼ ð Þð Þ¼ n  1 i¼1 rX rY ðn  1ÞrX rY

ð2Þ

In this paper, the moving window of n = 35 is used to calculate the eye movement signals of AF3 and AF4 channels. An eye movement can be recorded only if the correlation coefficient calculated by Eq. (2) satisfies the condition −1 < Rxy < −0.8. 2.2

The Complex Brain Networks

Nodes and edges are two important elements of a graph. The graph-based approach is to construct the brain network by using the various regions of the brain as a “node” and connecting the connections between the various regions of the brain as a “edge.” [9, 10]. In this study, we used the clustering efficiency and global efficiency of complex brain networks to analyze differences in brain function of operators. Clustering Coefficient. The connectivity of a node refers to the number of edges connected to the node, which can indicate the importance of the node in the network. It can be expressed as. Ci ¼

Ei Di ðDi  1Þ=2

ð3Þ

Where Ei is the number of existing edges between adjacent nodes of node i, and Di is the degree of connectivity of the node. Di(di − 1)/2 is the maximum possible number of edges between adjacent nodes of node i [10].

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3 Experiment 3.1

Subjects

Twelve healthy subjects (10 males and 2 females, age 26 ± 1.8 (s.d)) were randomly selected from the volunteers for the experiment. All subjects had no sleep related diseases. Moreover, no type of stimulant is allowed during the experience. Figure 3 shows the experimental set-up. 3.2

Experimental Process

The Emotiv, As a portable EEG acquisition device, the sampling frequency is 128 Hz. Use under actual driving conditions. The electrodes were pressed against the scalp according to the International 10–20 system (14 channels = AF3, AF4, F3, F4, FC5, FC6, F7, F8, T7, T8, P7, P8, O1 and O2). The test is carried out in the power plant workshop environment, and the surrounding environment has the characteristics of strong magnetic field, strong noise and frequent vibration. Each participant conducted two types of experiments. One type is that the subject observes the cross mark (Fig. 4) and then moves the eye to the end of the left yellow arrow. The eye movement process was completed in 0.5 s and then the subject moved the eyes back to observe the cross mark. The other type is that the subject observes the cross mark (Fig. 4) and then moves the eye to the right black arrow. The experimental procedure is the same as the previous experiment. The visual stimulation model is shown in Fig. 4.

Fig. 3. Experimental set-up

3.3

Data Preprocessing

In the experiment, the EEG signal is sensitive to noise. Therefore, signal noise must first be eliminated. In the present study, EEG signals of 0.5 Hz and 32 Hz were obtained by Butterworth high-pass and low-pass filters. The EEG signal is then divided into a low wavelet coefficient and a high wavelet coefficient. These low and high

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Fig. 4. The model of visual evoked stimulation

wavelet coefficients are further divided into a second high wavelet coefficient and a second low wavelet coefficient. From the original EEG signals we get d (0–4 Hz) and (4–8 Hz).

4 Results In the actual production site of the power plant, there are strong magnetic field, strong noise and mechanical vibration factors around the mid-set switch cubicle, which bring some difficulties to the identification of EEG motion characteristics. in this paper, the characteristics of the human brain network are combined with the characteristics of the eyeball motion signals in the moving image, and the characteristics of the human brain motion characteristic signals are comprehensively recognized. 4.1

Characteristics of Brain Network

Taking the brain signals of the main brain regions in this study as the research object, The correlation coefficient between each two electrode channel signals is calculated. and constructed a brain network, which was shown in Fig. 5.

10

8

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6

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Y

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0 -2 -2

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(a) Move to the left

8

-2 -2

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(b) Move to the right

Fig. 5. The brain networks when moving right and left

8

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As can be seen from Fig. 5, when the eye is rotated to the left, the connection density of the corresponding right brain network is significantly lower than that of the left brain. Conversely, when the eye moves to the right, the connection density of the left brain electrode is significantly lower than that of the right brain, which means that when the brain performs motor activity, the relevant brain region is in a neural activity state, and signal synchronism is poor. Figure 6 shows a comparison between clustering coefficients of brain network parameters and global variables as the subject moves left and right.

(a) Move to the left

(b) Move to the right

Fig. 6. The comparison of brain network parameters when a subject turns right or left

As can be seen from Fig. 6, for one subject, There is a significant difference between the brain network clustering coefficient and the overall efficiency corresponding to the left and right movement thinking imagination. In the experiment, when subjects are visually induced to move leftward, The density of attachment of the right hemisphere is less than the density of attachment of the left hemisphere, and the corresponding clustering coefficient and global efficiency value of the left cerebral hemisphere are greater than that of the right cerebral hemisphere. This means that the right hemisphere of the human brain is relatively active at this time, and each neuron cluster completes its own neural activity. So the correlation of neuron cluster activity in each brain region is relatively small, while the neural activity in the left hemisphere of the brain is inhibited, and the activities of each neuron cluster tend to be consistent and have relatively large correlation. 4.2

Comprehensive Judgment

In the actual production site of the power plant, there are strong magnetic field, strong noise and mechanical vibration factors around the central cabinet, which brings some difficulties to EEG signal acquisition, especially the recognition of EEG motion characteristics. In this study, the EEG motion characteristics were comprehensively recognized by combining the characteristics of eye motion signals and ERP-induced stimulus motion imagination. In our paper, we construct the brain network for the subsequent EEG signals, calculate the global efficiency and clustering coefficient corresponding to the brain network of the left and right hemispheres after recognizing

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that the time domain signal has left and right eye movement fluctuations by formula (2), and finally determine the movement direction according to the logical relationship between the two. Take leftward movement as an example, the discrimination logic is shown in Fig. 7 below.

Fig. 7. The logic of motion discrimination

5 Discussion In the experiment, the features of brain network and eye movement were used to identify human motion features. Although the recognition rate has reached a high accuracy rate, there are still some interferences and misjudgments, which bring some difficulties to the actual disassembly and assembly operation of the current transform in the mid-set switch cubicle. However, the accuracy rate can reach more than 97% by comprehensive discrimination of the two methods. The results are shown in Table 1. Table 1. Accuracy comparison of motion direction recognition Subject Subject Subject Subject Subject Subject Subject Subject Subject Subject Subject Subject

Brain network Eye movement Comprehensive test 1 80 90 100 2 85 95 100 3 75 85 95 4 85 100 100 5 90 90 100 6 85 100 100 7 85 100 95 8 90 95 95 9 85 90 90 10 80 85 100 11 85 90 95 12 100 100 100

From Table 1, it can be concluded that the comprehensive discrimination method based on human brain network features and eye movement signal features has high recognition accuracy for human left and right movements, which is suitable for recognizing EEG signals in power plant environment. In addition, the contrast between the

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brain machine auxiliary mode and the traditional disassembly and assembly mode is remarkable in the verification process of the actual power plant by adopting the method. In actual disassembly and assembly operations, the traditional disassembly and assembly mode requires two people, and it takes 4–5 min to complete one disassembly and assembly, while the brain machine auxiliary mode requires only one person, and it takes 5–6 min to complete one disassembly and assembly. It can be concluded that the working efficiency of the brain-computer aided mode is improved by nearly 60% compared with the traditional mode.

6 Conclusion In the actual maintenance of the power plant, the disassembly and assembly operation of that current transform in the mid-set switch cubicle, which is not easy for people to cooperate, is time-consuming and labor-consuming. In this study, we apply BCI technology to the actual operation. The method combines ERP visual evoked stimulation technology, adopts portable brain electrical acquisition equipment, collects EEG signals in real time, and extracts EEG motion characteristics. The result proves that the method can be well controlled the left and right motion adjustment of the robot, so that the screw holes of the current conversion and the screw holes of the fixed beam are always consistent. That makes the operation of disassembing or assembing a current transformer is easy and safety, which provides a new method for the maintenance of the mid-set switch cubicle in the power plant.

References 1. Broetz D, Braun C, Weber C et al (2010) Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: a case report. Neurorehabilitation Neural Repair 24(7):674–679 2. Rai R, Deshpande AV (2016) Fragmentary shape recognition: a BCI study. Comput Aided Des 71:51–64 3. Weisz J, Elvezio C, Allen P K (2013) A user interface for assistive grasping. In: 2013 IEEE/RSJ international conference on intelligent robots and systems. IEEE, pp 3216–3221 4. Bakardjian H, Tanaka T, Cichocki A (2010) Brain control of robotic arm using affective steady-state visual evoked potentials. In: Proceedings of the 5th IASTED International conference human-computer interaction, pp 23–25 5. Kennel M, Hinrichs H, Reichert C et al (2014) A robot for brain–controlled grasping. In: Workshop on HRI 2014, vol 3, p S4 6. Staufenbiel SM, Brouwer AM, Keizer AW et al (2014) Effect of beta and gamma neurofeedback on memory and intelligence in the elderly. Biol Psychol 95:74–85 7. Wang F, Wang H, Fu R (2018) Real-time ECG-based detection of fatigue driving using sample entropy. Entropy 20:196. https://doi.org/10.3390/e20030196 8. Cai SM, Chen W, Liu DB, Tang M, Chen X (2017) Complex network analysis of brain functional connectivity under a multi-step cognitive task. Phys A Stat Mech Appl 466:663–671 9. Kar S, Bhagat M, Routray A (2010) EEG signal analysis for the assessment and quantification of driver’s fatigue. Transp Research Part F Traffic Psychol Behav 13(5):297–306 10. Messé A, Marrelec G, Bellec P et al (2012) Comparing structural and functional graph theory features in the human brain using multimodal MRI. IRBM 33(4):244–253

Ship Detection Based on Faster R-CNN Network in Optical Remote Sensing Images Min Zhai1,2(&), Huaping Liu2, Fuchun Sun2, and Yan Zhang3 1

2

The State Key Laboratory of Astronautic Dynamics, Xi’an Satellite Control Center, Xi’an, China [email protected] The Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China [email protected] 3 Xi’an Satellite Control Center, Xi’an 710043, China

Abstract. A ship detection model based on Faster R-CNN is proposed for ship detection tasks in optical remote sensing images. Deep convolutional neural network could replace traditional manual design feature to extract ship features automatically and quickly from makes the detection performance of ship no longer relying on the design of artificial features. This paper proposes a strategy that combines the model with two different size of convolution neural networks respectively. Experiments on datasets HRSC16 verify the models detection capabilities and the mean average precision can achieve 78.2%. For the problem of low recall rate in the detection of adjacent vessels, this paper adopts the SoftNMS method. Compared with the traditional NMS, the Soft-NMS method can electively improve the model detection performance to 80.1%. At the same time, it also shows that the model we proposed is a robust model and has a certain degree of generalization ability. Keywords: Ship detection


Faster R-CNN
Deep learning
Soft-NMS

1 Introduction Ship detection has become an increasingly important area, playing a more and more important role in military and civilian fields, such as: land and sea resources scheduling, marine transportation, marine safety and so on. In earlier years, Synthetic Aperture Radar (SAR) images (see in [1]) were usually used for ship detection because they were not affected by light and clouds and could be seen in all kinds of weather. However, one disadvantage of SAR images is that they provide less details on ships and lack color details, which is very important for ship detection, leading to low ship detection accuracy. In recent years, the development of technologies in remote sensing images has made it possible to get many remote sensing satellite images, many researchers have studied the problem of ship detection in optical remote sensing images.

© Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 22–31, 2020. https://doi.org/10.1007/978-981-32-9050-1_3

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The existing methods are mainly a coarse to fine detection process, which are generally divided into two phases: image preprocessing stage and ship identification stage. The image preprocessing stage mainly includes three operations: image augmentation, cloud removal and sea-land segmentation. The stage of ship identification mainly includes the extraction of candidate and the ship detection. Yang in [2] proposed a model based on the analysis of sea level. Zhang in [3] proposed a method for comprehensively utilizing the SIFT feature points and Harris corners for class identification of ships, which achieves high recognition rate. Zhai in [4] proposed a detection method based on tower keyword histogram and support vector machine. The method can be directly used to classify UAV aerial images or camera images to improve detection speed and accuracy. Li in [5] proposed a method for detecting ships using multi-layer sparse coding. A common disadvantage of these traditional methods is that the accuracy of ship detection depends on the quality of the manual designed features. On the other hand, since the amount of remote sensing images we can gather has grown rapidly, traditional methods can no longer meet the needs of remote sensing image detection under big data. Deep learning has developed rapidly in recent years and has made it possible for the problem of ship detection. Methods such as Faster RCNN [6] based on the idea of the region proposal and YOLO (see in [7]) and SSD (see in [8] based on the regression idea have achieved good performance in object detection. Lin in [9] use the pixel-based FCN network for ship detection, but the small target detection capability is weak, the false alarm rate is high. Zou in [10] used CNN (convolutional neural network) to construct SVD (singular value decomposition) Network for ship detection based on singular value decomposition. Based on Fast RCNN, Liu in [11] used SRBBS (Ship Rotated Bounding-Box Space) as a candidate area generation network for ship detection. Liu in [12] proposed the RR-CNN network based on the Rotated Region. The network can provide more accurate bounding box positions, but the detection accuracy is lower on the public dataset. Since there are not many studies on the use of the Faster R-CNN for ship detection in remote sensing images, a model which is based on Faster R-CNN is proposed to solve the problem of ship detection in optical remote sensing images. Considering that there is a low recall rate when detecting adjacent ship, we use a new NMS method named Soft-NMS [13] to suppress overlapping bounding boxes. The rest of the paper is organized as follows: the related work is introduced in Sect. 2; the detail information of our model is introduced in Sect. 3; the experimental results are shown in Sect. 4 and the conclusion is presented in Sect. 5.

2 Principle of Faster R-CNN Network In this paper, the model we proposed is based on Faster R-CNN framework. Compared with Fast R-CNN, the main improvement is that the region proposal also uses a convolutional neural network. For simply understanding, Faster R-CNN network can be thought as two parts: a region proposal network named RPN and a detection

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network named Fast R-CNN network. In Sect. 2.1, the details of RPN are introduced and in Sect. 2.2 how both parts share convolution features in training and test stage are introduced. 2.1

RPN

RPN is used to generate region proposals. The purpose of the RPN network is to directly derive the slide window directly from the convolutional feature map with indefinite positions and sizes, thereby alleviating the calculation pressure of the subsequent network. By adding a RPN network, the training model is simplified, and the speed of training and detection are improved. In RPN structure, Faster R-CNN introduces an anchor mechanism, which generates 3 * 3 anchor points for each sliding window. Therefore, for the bounding box regression layer, each sliding window has 4 * 9 position outputs. For the classification layers, each sliding window has 2 * 9 categories of output. 2.2

Sharing Convolution Features for RPN and Fast R-CNN

In this paper, Fast R-CNN [15] is used as our detection network. During the entire Faster R-CNN network training process, in order to simplify the model and improve training efficiency, the convolutional layer calculation between the RPN and Fast RCNN is shared. The whole training process can be divided into 4 steps: First, we train the RPN which is initialized with an model which is pre-trained on ImageNet and then our RPN will be fine-tuned for region proposal; Second, we train the detection network, during the training process, the proposals generated by RPN will be used; Third, the RPN is initialized by the detection network; At last, the layers of Fast R-CNN will be fine-tuned. The two networks share the same convolutional layers in this way. The training accuracy and speed are improved. The ZF network and VGG16 network are used as the backbone. An pre-trained model on ImageNet is used to initialize the training network.

3 Methods 3.1

Data

Because of the lack of public remote sensing datasets for ship detection, many researchers do their experiments on images collected from Google Earth or other highresolution satellites, such as QuickBird, GF-1, GF-2 satellite. In our experiment, we use the data set named HRSC2016 [14]. HRSC16 dataset contains 1070 images collected from Google Earth which includes ships inshore and on the sea. The image sizes differ from 300  300 to 11500  900. The whole dataset is splited into training sets with 1207 samples in 443 images, validation sets with 541 samples in 183 images, and test sets with 1071 samples in 444 images.

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The Detection and Classification System

Our ship detection model is seen in Fig. 1, which can achieve an end-to-end detection system based on deep learning method. After experiments, VGG16 network is chosen as the base network, which is easy to be fine-tuned. As discussed in Sect. 2.1, a RPN network is used to gather region proposals with different scales and aspect ratios. Fast R-CNN [15] is adopted as the detection network. By using the suitable training method, both CNN network can share their convolutional layers, which improves running speed and detection accuracy.

Fig. 1. Ship detection and classification system.

3.3

Training

The system is 64-bit ubuntu; GPU: NVIDIA GTX 1080 Ti; deep learning framework: caffe; program language: python. The initial learning rate is set to 0.001 and is divided by 10 on the 120000th iterations and the 240000th iterations. Loss Function. The loss function of our model is defined as Eq. (1): Lð p i ; b i Þ ¼

    1 X 1 X  Lcls pi ; pi þ k pi Lreg bi ; bi Ncls i Nreg i

ð1Þ

Here, i means the number of the anchor, pi is the predicted probability whether an anchor is going be on object. pi has 2 values: 1 when the anchor is positive and 0 in other situations. bi is represents the bounding box which is predicted by our model, and bi represents the ground truth bounding box. Ncls is usually normalized by the size of a batch and Nreg is usually normalized by the number of anchors. k is usually set 10. Lcls

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represents the classi_cation loss and Lreg represents the regression loss, described as follow:   Lreg ¼ R bi  bi where R is the L1 function de_ned as Eq. (2) which is less sensitive to outliers compared with L2 loss.  0:5x2 ; if j xj \1 smoothL1 ð xÞ ¼ ð2Þ j xj  0:5; otherwise For bounding box regression, we parameterize the coordinates as Eq. (3) bd x ¼ ðdx  dxi Þ=wi ; bd y ¼ ðdy  dyi Þ=hi bw ¼ logðw=wi Þ; bh ¼ logðh=hi Þ bpdx ¼ ðdxp  dxi Þ=wi ; bpdy ¼ ðdyp  dyi Þ=hi

ð3Þ

bpw ¼ logðw =wi Þ; bph ¼ logðhp =hi Þ Where ðdx; dyÞ represents of the bounding box’s center coordinates, ðh; wÞ denote the bounding box’s width and height. ðdx; dy; h; wÞ represents the ground truth bounding box, ðdxp ; dyp ; hp ; wp Þ represents the predicted bounding box, and ðdxi ; dyi ; hi ; wi Þ represents the anchor bounding box. 3.4

Testing

NMS. NMS (non-maximum suppression) is often used to generate the final detection results. The traditional NMS method uses a greedy strategy. A simple understanding is that we locally search for maxima and suppress non-maximal elements. In the process of object detection, we assume that the list of detected bounding boxes is B, the list of confidence scores corresponding to each bounding box is S, and the final result of the detection is D. The traditional NMS method first select the bounding box b which has the maximum score N , and then removes b from B. The bounding box b will be put into D. Any bounding box whose overlap is greater than the threshold Nthreshhold in B will also be removed by setting the score of the bounding box to zero. This process is repeated until the list of B is empty. However, for the detection of adjacent ships, when one ship is detected, the adjacent ship is actually present in the overlap threshold, which will be suppressed and lead to a low recall and a drop in average precision. In this paper, a different NMS method will be used. Instead of simply setting the score of the other bounding box whose overlap is greater than the threshold Nthreshhold to zero, the scores will be set as a function of its overlap with N . It is defined as follows:  if iouðN ; bi Þ [ Nthreshhold si; si ¼ ð4Þ si ð1  iouðN ; bi ÞÞ; otherwise s i ¼ e

iouðN ;bi Þ r

2

; 8bi 62 D

ð5Þ

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si represents the score of each bounding box, iouðN ; bi Þ represents the overlap between the bounding box which has the maximum score N and other bounding boxes in list of B, Nthreshhold represents the iou threshold. Equation (4) is a linear function of overlap between the two bounding boxes. It means that bounding boxes whose overlap with the bounding box which has the maximum score N will not be affected. Considering the continuity of the function, we also adopt another function, which is a Gaussian function, as shown in Eq. (5). The recall rate and the average precision can be improved by this method in adjacent ship detection.

4 Experimental Results and Analysis 4.1

Faster R-CNN Results and Analysis

Experiments have be done on HRSC16 provided by [14]. The ZF [15] netand the VGG16 [16] net are used in our experiment. The results in [14] is used as the baseline. Mean average precision(mAP) is used as the criteria. Table 1 shows the results of the experiment we have done. From Table 1, it can be seen that the mAP of our model is signi_cantly higher than [14]. Performance of our model that uses VGG16 net is much better than that with ZF net. The main reason is that VGG16 net is suitable for large scale images than ZF network. Images in our data set are usually larger than 1000  600. So we get better performance than using ZF network. We can see that Faster R-CNN +VGG16 outperforms Faster R-CNN +ZF and [14] by +1.2% and +5.5% mean average precision respectively.

Table 1. mAP of our model on HRSC2016 data set Ship. [14] 45.1% ZF 46.2% VCG16 54.1%

Mer. 74.1% 75.1% 78.7%

War. 89.4% 89.6% 90.0%

Air. 87.2% 88.0% 90.5%

mAP. 72.7% 73.9% 78.2%

We also use the precision and the recall rate of the detection results to evaluate our model, which are computed as follows: precision ¼ NTPNþTPNFP recall ¼ NFPNþTPNFN

ð6Þ

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Where NTP represents the number of true positives, NFP represents the number of false positives, and NFN represents the number of false negatives. The results of our model with VGG16 net is show in Fig. 2.

(a) Aircraft carrier.

(b) Warcraft.

(c) Merchant Ship.

(d) Ship.

Fig. 2. The Precision-Recall curve of four object detection classes

Figure 2 and Table 1 both show that aircraft carrier and warcraft detection results are much better than merchant ship and ship. Examining images of the four object classes in our data set we find that samples of aircraft carrier and warcraft are large scale and have uniform class distribution. However, samples of merchant ship and ship have different size and different appearance, which leads to a lower mean average precision compared with the other two classes. Some detection results of our model are presented in Fig. 3. It can be seen that our model has a good detection and classification ability from the results. The false alarm rate is greatly reduced. From the top row, we can see that even in the condition with mist or wave, our model still can detect ships without image preprocessing, which shows our model is robust.

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Fig. 3. Some detection results of our model

4.2

Sof-NMS Result and Analysis

As the last two pictures of the bottom row shown in Fig. 3, when we detect the adjacent ships, a true positive get suppressed with traditional NMS, which leads to a lower average precision and recall rate. In our model, we use soft-NMS replaced of traditional NMS. We perform experiments on HRSC2016 with Faster R-CNN + VGG16 Net. The results can be seen in Table 2. Table 2. Results on HRSC2016 for Faster R-CNN + VGG16 Net with different NMS methods Ship. Mer. War. Air. mAP. Traditional NMS 54.1% 78.7% 90.0% 90.5% 78.2% Soft-NMS 55.3% 83.4% 90.8% 91.0% 80.1%

Figure 4 show some detection results of our model with Soft-NMS. In Fig. 4, when we detect the adjacent ship, the bounding box will be suppressed with traditional NMS, which will not happen with Soft-NMS method. From Table 2, we can see that Soft-NMS obtains an improvement of 1.9% mean average precision. We also observe that Soft-NMS improves maximum performance of 4.7% object detection classes. The main reason is that there are more adjacent samples of merchant ship than samples of other categories in our data set.

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Fig. 4. The results of adjacent ship detection with different NMS methods, the left images are the results of traditional NMS, while the right images are the results of Soft-NMS

5 Conclusions In this paper, we propose a ship detection model in optical remote sensing images based on Faster R-CNN network. Through the public data set HRSC16, we have obtained an ship detection model which is an end-to-end detection model. When dealing with the adjacent ship detection, we use Soft-NMS instead of traditional NMS, which effectively solves the problem of adjacent ship detection and obtains an improvement of 1.9% mean average precision and an improvement of 4.7% average precision for merchant ship in the four object detection classes. And in our detection pipeline, we do not need to do image pre-processing, such as removing cloud and landsea segmentation. The mean average precision can reach 80.1%, which has an improvement of 6.4% compared with the results in [14]. There is still room for improvement in terms of small ship detection. This is our future work direction.

References 1. Eldhuset K (1996) An automatic ship and ship wake detection system for spaceborne SAR images in coastal regions. Geosci Remote Sens IEEE Trans 34(4):1010–1019 2. Yang G, Li B, Ji S, Gao F, Xu Q (2013) Ship detection from optical satellite images based on sea surface analysis. Geosci Remote Sens Lett 11(3):641–645 3. Zhang S, Li YX, Zhou ZM (2016) Ship recognition in optical remote sensing image. J Shanghai Jiaotong Univ 50(9):1415–1421 4. Zhai Y, Chi W, Jin L (2017) Real-time detection method based on PHOW for ship images classification. Sci Technol Eng 17(33):131–135 5. Li Z, Yang D, Chen Z (2015) Multi-layer sparse coding based ship detection for remote sensing images. In: IEEE international conference on information reuse and integration, pp 122–125

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6. Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149 7. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Computer vision and pattern recognition, pp 779–788 8. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) SSD: single shot MultiBox detector. In: European conference on computer vision, pp 21–37 9. Lin H, Shi Z, Zou Z (2017) Fully convolutional network with task partitioning for inshore ship detection in optical remote sensing images. IEEE Geosci Remote Sens Lett 14(99):15 10. Zou Z, Shi Z (2016) Ship detection in spaceborne optical image with SVD networks. IEEE Trans Geosci Remote Sens 54(10):5832–5835 11. Liu Z, Wang H, Weng L, Yang Y (2017) Ship rotated bounding box space for ship extraction from high-resolution optical satellite images with complex backgrounds. IEEE Trans Geosci Remote Sens 13(8):1074–1078 12. Liu Z, Hu J, Weng L, Yang Y (2018) Rotated region based CNN for ship detection. In: IEEE international conference on image processing, pp 900–904 13. Bodla N, Singh B, Chellappa R, Davis LS (2017) Improving object detection with one line of code 14. Liu Z, Liu Y, Weng L, Yang Y (2017) A high resolution optical satellite image dataset for ship recognition and some new baselines. In: International conference on pattern recognition applications and methods, pp 324–331 15. Girshick R (2015) Fast R-CNN. In: IEEE international conference on computer vision, pp 1440–1448 16. Karen S, Andrew Z (2014) Very deep convolutional networks for large-scale image recognition. Comput Sci

An Efficient Real-Time Indoor Autonomous Navigation and Path Planning System for Drones Based on RGB-D Sensor Ran Xiao1,2(B) , Hao Du1,2 , Chaowen Xu1,2 , and Wei Wang1,2,3

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1 Institute of Applied Research Intelligent Science and Technology, Jiangsu and Chinese Academy of Sciences, Changzhou 213164, Jiangsu, China [email protected] 2 Changzhou Research and Industrialization Center for Advanced Manufacturing Technology, Chinese Academy of Sciences, Changzhou 213164, Jiangsu, China School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China

Abstract. An efficient real-time autonomous drone navigation and path planning system is proposed by using an RGB-D sensor in the GPSdenied indoor environment. Firstly, the RGB images and their corresponding depth images collected by the sensor were used for real-time pose estimation and dense point cloud map reconstruction based on ORB-SLAM (Simultaneous Localization And Mapping system based on the ORiented BRIEF feature). With the attitude angle data from the Inertial Measurement Unit (IMU) used in the pose initialization, the SLAM system can provide a more accurate pose information, and therefore a better 3D map when the drone flies with a high speed. In the second phase, a simple and efficient pathfinding program was developed. In this procedure, the point cloud map data would be translated to a map based on the octree structure, which contains the occupied and free voxels. The size of occupied nodes in the octree map is three times smaller than that of free nodes, which ensures more free area for the drone’s flight. Every free voxel in the octree map was set to have enough space for the drone to cross. An improved A∗ algorithm map was used to plan the route based on the octree. The simulation result showed that this efficient pathfinding method can reduce execution time and memory space significantly. In the actual flight, a local path planning system was proposed to avoid obstacles in real time. Keywords: Indoor Visual SLAM · Drone Collision avoidance · Path planning

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· Octree ·

Introduction

With the increasing of the natural and man-made disasters in recent years, the prediction and prevention of disasters have received widespread attention. At the c Springer Nature Singapore Pte Ltd. 2020  Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 32–44, 2020. https://doi.org/10.1007/978-981-32-9050-1_4

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same time, to organize effective rescues and to reduce the losses after disasters have become very important. The small-size drones have been proved as a useful tool during missions of search and rescue [1], fire-fighting [2], target tracking [3], intelligent patrol [4], and surveillance [5]. Compared with helicopters and ground robots, drones have a lot of merits in the complex surroundings: light weight, small size and better dynamic performance and so on. Currently, for flood and earthquake disasters, small and medium-sized fixed-wing or multi-rotor drones are mostly used to carry out on site investigation; by collecting image information of the affected areas, the disaster can be quickly and accurately detected, assessed, and processed. However, it is still difficult to investigate interior situations of the GPS-denied damaged buildings. In these environments, drones should have the ability of autonomous navigation and 3D map reconstruction of surroundings (namely SLAM) and the ability of path planning. SLAM is the key technology to improve the intelligent performance of robots, while the localization technology enables drones to know where they are, so that they can execute tasks in the unknown environment; the 3D scene reconstruction technique can not only provide necessary information for localization, navigation and obstacle avoidance, but also enable drones to perceive surroundings and identify objects. The sensors used in the current SLAM research are always lasers, cameras or multi-sensors [6–8]. Although lasers can provide the accurate range information, they are usually very expensive and heavy and not suitable for low-cost and small-size indoor drones. Cheap and light-weight cameras are a best choice for the SLAM research. Mur-Artal et al developed a convenient SLAM system for Cameras [9], but the sparse point cloud map they constructed just can provide optimization information for localization and not appropriate for the clear scene recognition and pathfinding. Lv et al [10] provided dense 3D reconstruction and the octree map based on ORB-SLAM, but they did not consider the path planing based on the 3D map. In our system, an RGB-D sensor is used in the drone (see Fig. 1) to construct both the dense point cloud map and the octree map for surveillance and pathfinding; the data of roll, pitch and yaw from the IMU provide the initial attitude information, which improves the accuracy of the pose estimation and the 3D map construction in the non-linear flight with high speed. The popular path planing methods are mainly based the graph theory (such as Voronoi diagrams [11] and PRM [12]) and grid algorithm (e.g. A∗ and Dijkstra [13] algorithm). These methods may work well in the simple 2D map, but in the complex 3D environment, they are always time-consuming and sometimes can not find an efficient path. The octree map, where the 3D space is recursively subdivided into eight octants, can compress the memory space and its resolution is adjustable according to the practical requirement. Yan et al [14] gave a PRM pathfinding method based on octree algorithm, but the computation of the connectivity evaluation in each bounding box is very complicated. V¨ o r¨ o s [15] provides a highly efficient method for the neighbor searching by using the octree matrix instead of a voxel to represent the octree map. In the matrix octree data, each octant or node is expressed by an n × 3 matrix, where n means

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the octree depth level and the 3 digits in each row indicate a number in base 8 of a binary form of the corresponding level of the octree map. Based on this matrix octree representation, Rodenberg et al [16] proposed an A∗ pathfinding method through a matrix octree representation of a point cloud map, they considered all the free-region neighbors including sharing faces, edges and vertexes, the disadvantage is the requirement of calculating the maximal crossing value of neighboring empty nodes.

Fig. 1. The drone equipped with the ASUS Xtion Pro-Live camera in this work.

In this research, an RGB-D sensor is used to estimate the real-time pose of the indoor drone based on ORB-SLAM, meanwhile the 3D dense point cloud map with color information is constructed for environment exploration. With the help of the pose initialization data from IMU, an accurate trajectory and a globally consistent map are achieved. In this SLAM method, IMU just provides the initial angle data in every pose estimation process, which can eliminate the effect of the IMU drift and give a precise initial angle change for the pose estimation. After completing the model construction, the saved point cloud data are compressed to the octree map represented by matrix [15] with an optimal free space resolution three times larger than the resolution of the occupied space, where the free node resolution is a little larger than the drone’s maximum physical size. In the pathfinding process, the equal-size free face neighbors of the current node are considered. This method can not only give much more free space but also can make the drone pass through the free space safely. Then the A∗ algorithm is used at the start and the goal at the same time for the offline path planning process, we named this method as bidirectional A∗ method. The simulation results showed that this method reduced a lot of pathfinding time compared with the normal A∗ algorithm. In the actual flight, a dynamic obstacle avoidance drone system is proposed, which ensures the drone to replan the path in real time when some dynamic unexpected obstacles appear in the originally planned path.

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RGB-D SLAM System

ORB-SLAM is a precise visual-based SLAM system by using feature matching and nonlinear optimization methods [9]. It can be realized with monocular, binocular and RGB-D cameras. This SLAM system takes long time to initialize and suffers from scale drift when the monocular camera is used. For the binocular camera, the inaccuracy of the disparity calculation is a major disadvantage for the SLAM pose estimation. All there issues can be solved by using an RGB-D sensor, because this camera can provide the depth information of the feature point. In this paper, we build our indoor SLAM system by using an Xtion Pro-Live camera based on ORB-SLAM. The accurate pose estimation and dense and colorful point cloud map are constructed in real time on the on-board minicomputer in this process.

Fig. 2. Indoor SLAM work flow

In the flight experiment, the ORB-SLAM frame can work well with the only RGBD sensor when the drone flies slowly. But the 3D map is in a mess when the drone flies with a high speed (>5 m/s) especially in the non-linear flight. So an SBG IMU (Ellipse2-N) is used in the SLAM system to help improve the mapping performance, for the detailed work flow please see Fig. 2. The RGB-D camera provides both the RGB frame and depth frame (D frame) with 30 Hz, the ORB features are extracted from the RGB frame and then the corresponding depth information from the D frame is picked up to generate the 3D feature points. In the tracking period, the first step is to initialize pose with three modes: motion model, reference key frame, and relocalization. The attitude angle information from the IMU replaces the original estimated angle data as the initial pose in these three modes. The original initial pose is a transformation matrix Tini which composes a rotation matrix Rint and a translation vector tint , as shown in Eq. (1):     Rini tini Rimu tini Tini = ; Tnew = ; (1) OT 1 OT 1

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For the transformation matrix (Tnew ) in our research, the rotation matrix Rini in the initial pose Tini is replaced by the Rimu , where Rimu is the rotation matrix derived from the values of roll, pitch, and yaw from the IMU. Then the computation system begins with three main parallel threads: tracking, local mapping and loop closing. Finally, the global BA (Bundle Adjustment) is used to optimize the pose and the global map in the fourth thread. In this research, the color information of the points of the key frame is extracted in the tracking thread, and these colorful frames are used to construct the dense 3D colorful map by optimal transformation using their optimized pose matrix in the world coordinate system.

Fig. 3. The trajectories and 3D maps before and after using the IMU data. Figure (a) and (b) give the trajectories with and without IMU data, respectively. Figure (c) and (d) present the 3D dense maps with and without IMU data, respectively.

The indoor SLAM experiments are carried out with and without IMU data, respectively, and the results of the drone trajectory of 6 circles around a table and the indoor map are shown in Fig. 3, where the table is 1.5 m in the width and 6 m in the length. From Fig. 3(a) and (b), it is clear to see that with the help of the IMU data, the trajectory is more accurate than that without using IMU, and the pose computation rate is about 17 to 20 Hz, which can meet the

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requirement of the real-time flight. For the map construction in the bottom of Fig. 3(c) and (d), it is shown that the map without using IMU data is not globally consistent, this map is too messy to be used for scene investigation and the drone navigation. Apparently the map constructed with IMU data is better for this SLAM system.

3 3.1

Pathfinding System Based on the Octree Map Brief Introduction of the Octree Map

The octree algorithm is used to divide the 3D space to eight same-size cubes recursively till it reaches the maximum recursive depth N . Each cube or octant in its parent node can be located at one of the two sides of the split plane, which will be indicated by either a 0 (after/left/above of the split plane) or 1 (before/right/up of the split plane) in x-,y-, and z-dimension. In Fig. 4 (a), for depth level 1, the eight octants can be represented as [0,0,0], [1,0,0], [0,1,0], [1,1,0],  [0,0,1],  [1,0,1], [0,1,1],   [1,1,1];  for level 2, the eight children of node [1,0,1] 1, 0, 1 1, 0, 1 1, 0, 1 are , , and so on; for level 3, the children of node 0, 0, 0 ⎡ 1, 0, 0⎤ ⎡ 0, 1, 0 ⎤ ⎡ ⎤   1, 0, 1 1, 0, 1 1, 0, 1 1, 0, 1 are ⎣ 1, 1, 1 ⎦ , ⎣ 1, 1, 1 ⎦ , ⎣ 1, 1, 1 ⎦ , ... . And for level n, each octant 1, 1, 1 0, 1, 0 0, 0, 0 1, 0, 0 ⎡ ⎤ x1 , y1 , z1 ⎢ ⎥ ... ⎢ ⎥ ⎢ can be represented as a matrix⎢ xi , yi , zi ⎥ ⎥ , where xi , yi , and zi represent 0 ⎣ ⎦ ... xn , yn , zn or 1 (1 < i < n), [xi , yi , zi ] is the ith number of a node in level n. The node can be encoded with only 3 binary bits for every level in the octree map. Each 3-bit number can be simplified to a single number in base 8 (from 0 to 7), where zi and xi are the most and least significant bits, respectively. At this point each free or occupied octant in different levels and different positions can be replaced by an integer, if the level is n, the node code contains n integers, the code of the parent node is put before the child node code, for details please see Fig. 4(b). For example, the eight octant codes of node of “5” (n = 1) are 50 to 57 (n = 2). We just need to save the digital labelled cells instead of the dense point cloud map. In this octree map, if a node has obstacle points inside, whether these points occupy the node completely or not, this node is considered as an occupied node; and the node which is not occupied by the obstacle is called the free node or empty node. It is obvious that the larger the resolution of the nodes is, the smaller the free area can be used in the flight area. The small octree map resolution can give more free region for the drone, but the drone needs enough space to pass through each node safely. So two different resolutions are used for the occupied nodes and free nodes. In our research, if the eight face neighbors of the initial free node are free, this node is considered as a new free node.

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Therefore, the new free node resolution is three times larger than the occupied node. And only these larger size free nodes are used as the area for pathfinding. All the free nodes mentioned below are large size free nodes.

Fig. 4. Subdivision of an octree. Figure (a) and (b) give the matrix and digital code representation of the octants, respectively. White octants mean free nodes, and gray octants are occupied nodes.

3.2

Pathfinding Based on A∗ Algorithm

The free nodes collected in Sect. 3.1 by using the octree subdivision method are used for the drone path planning. In our research, both the global off-line and the real-time pathfinding methods are used in the drone to improve the performance of dynamic obstacle avoidance and the safety of real-time planning. Global Bidirectional A∗ Pathfinding Algorithm. The free area separated out from the map are used for the A∗ path-finding method, which can speed up the efficiency of the path planning. The principle of the normal A∗ algorithm is as follows in Eq. (2): f (M ) = g(M ) + h(M ) (2) where f (M ) is the estimated cost from the start node S to the goal node G through the current node M , g(M ) is the practical cost from S to M , h(M ) is the estimated cost from M to G, in this research it computes the Manhattan distance between M and G. In the path planning, the next node with the minimum total cost f (M ) is chosen. According to the octree division method, the map will be processed as a big cube, this cube is normally larger than the actual map, and accordingly the collected free area is always outside of the drone’s flight scope. So only these free nodes in the octree map inside the 3D map are taken into account. What’s more, only the equal-size free face neighbor nodes are considered to enable the drone to pass safely and improve the pathfinding

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efficiency simultaneously. In the off-line path planning phase, unlike the conventional A∗ method, we apply the bidirectional A∗ algorithm to search the route from the departure and destination points parallelly, the specific steps are show in Algorithm 1. In this algorithm, OpenSet1 and OpenSet2 are two sets to save the not evaluated nodes, CloseSet1 and CloseSet2 are two sets to save nodes which are already evaluated, ComeF rom1 and ComeF rom2 are to save the relation between the current node and its previous node with the minimum actual cost. And g1(N 1), g2(N 2) are the actual costs from S to node N 1 and from node G to node N 2, respectively, where N 1 and N 2 are the current nodes of the bidirectional A∗ method from S and G; f 1(N 0) and f 2(N 0) describe the total costs from S to N 2 and from G to N 1 through node N 0, respectively. Distance(N 1, N 0) computes the distance from N 1 to N 0, neighbors(N 1) gives the face neighbors of N 1 in the scope of free nodes. By using this search method, the path data are saved in vector P by the combination of two routes from S and G, and the final global path graph P is sent to the drone’s flight controller. In order to prove the advantage of the bidirectional A∗ algorithm, both the path planning performance of the bidirectional A∗ and the normal A∗ algorithms are computed in our research in a complicated map with the area of 100 × 100 × 50 m3 and with the map point number of 209,361. In the simulation, we assume that the drone flies from start point (13, 92, 45) m to the goal point (97, 10, 3) m. We simulate these two pathfinding methods in different free node octree map resolutions of R, 2R, 4R and 8R respectively, where R is 0.38 m. The computer system in this simulation has an Intel Core i7-4790 CPU (3.60 GHz) processor and 8.00 GB of RAM with Ubuntu14.04 64 bits. The simulation result in Table 1 shows that the bidirectional A∗ and the normal A∗ method have the almost same path distance in different resolutions. The number of the empty leaf nodes and the computation time of different octree map resolutions are shown in Fig. 5. From this figure, it is obvious that the larger the resolution is, the less the empty node number is, and the less the flight area (resolution × (empty node number)) is. The computation time also varies inversely with the octree map resolution. In order to take account of both computation time and the flight area, the free node resolution is larger than the occupied node’s and the free node resolution is set as a value which is a little bigger than the drone’s physical size to enable this drone to pass through safely. Comparing the computation time of the A∗ and bidirectional A∗ methods, we can see that the former is almost one order of magnitude larger then the latter. And the smaller the resolution is, the larger of the two kinds of computation time difference value is. According to the above simulation analysis, the bidirectional A∗ method is used for searching an efficient route in the global pathfinding. And the result of the global path planning in the real indoor environment is expressed in Fig. 6(a), where the map size is 6.71 × 12.58 × 3.16 m3 and the size of the free node is 0.80 m. Figure 6(b) shows the octomap of the indoor environment with the occupied node resolution of about 0.27 m.

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Algorithm 1. Bidirectional A∗ algorithm OpenSet1.push(S); OpenSet2.push(G) CloseSet1=empty set; CloseSet2=empty set ComeF rom1=empty map; ComeF rom2=empty map g1[S]=0; g2[G]=0 f 1[S]=Distance(S, G); f 2[G]=Distance(G, S) while OpenSet1!=Null & OpenSet2!=Null do N 1=Node in OpenSet1 with smallest f 1; OpenSet1.remove(N 1) // Extract the node with smallest f 1 and remove this node from OpenSet1 N 2=Node in OpenSet2 with smallest f 2;OpenSet2.remove(N 2) if N 2 in neighbors(N 1) then return ComeF rom1, ComeF rom2, g1, g2 end if for N 0 in neighbors(N 1) do gnew=g1(N 1)+Distance(N 1, N 0) //gnew is the new actual cost if N 0 not in CloseSet1 or gnew < g1(N 0) then g1(N 0)=gnew f 1(N 0)=g1(N 0)+Distance(N 2, N 0) OpenSet1.push(N 0); CloseSet2.push(N 0);ComeF rom1[N 0]=N 1 for N 0 in neighbors(N 2) do gnew=g2(N 2)+Distance(N 2, N 0) if N 0 not in CloseSet2 or gnew < g2(N 0) then g2(N 0)=gnew f 2=g2(N 0)+Distance(N 1, N 0) OpenSet2.push(N 0);CloseSet2.push(N 0);ComeF rom2[N 0]=N 2 end if end for end if end for end while

Table 1. The path distances of different octree resolutions. Resolution/m R Path cost/m

2R

4R

8R

207.68 208.05 208.83 213.47

Dynamic Obstacle Avoidance System Proposal. The former part of Sect. 3.2 gives the off-line global path planning. This method is adequate for the static environment, but in the actual flight, there are always some dynamic obstacles. So the local dynamic path planning method in the drone’s flight is proposed as in Fig. 7. In the task of flight mission, the drone flies according to the path P from the start point S and detects the surrounding environment in real time. Then the on-board computing system judges whether there are obstacles in the area of next 1 to T nodes after the current node in the current path, where

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Fig. 5. The empty leaf node number and the computation time of different octree map resolutions where R = 0.38 m.

T ∗ R is the safe distance of the drone to obstacles and R is the size of the empty node. If there is no obstacle until the drone arrive at goal point, this real-time navigation ends successfully. If there are obstacles in the path, these occupied nodes will be set as set O. The next step is to search out whether there is a route from the current node N to the next node in the path graph P skipping the set O to the destination G by using the normal A∗ method. If there is a route, the node is set as W , and the original route is repaired by replacing the path from current node N to node W by the new path, noted as new path P . And then the drone will fly along path P and detect the surrounding environment in a circular manner in real time till it arrives at point G. If there is no new path, this pathfinding process ends in failure.

4

Discussion and Summary

In this paper, we discussed the vision-based SLAM system based on ORB-SLAM and the real-time path planning system with an RGB-D camera. The SLAM process is very critical because an accurate and perfect map is a prerequisite for the path planning. The experiment results showed that the trajectory and the point cloud map were more accurate with the IMU data using in the pose initialization process. After reconstructing the colorful dense point cloud map, in order to increase the efficiency of the A∗ pathfinding method, we use the octree algorithm to divide the space to the empty area and the occupied area. In the octree map, the empty node’s resolution is three times larger than the occupied node’s, so that more space can be used for the drone to pass through

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Fig. 6. The global path planning in indoor environment. In Figure (a), the magenta square area give the flight area from the start point to the goal point. Figure (b) gives the octomap of the indoor environment.

safely. Two kinds of the A∗ algorithm are used in the process of pathfinding: the bidirectional A∗ algorithm and normal A∗ algorithm. The former method is used for the global path planning, which starts the A∗ method from the start and goal points parallelly and improve the path planning efficiency compared with the normal A∗ algorithm starting from one point. The latter method is used when the drone flies to avoid obstacles in real-time in the real flight operation in the local path planning. The global path-finding simulation in the complicate map with different octree resolutions shows that the bidirectional A∗ method can improve the computation time significantly.

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Fig. 7. The path-finding method with dynamic obstacle avoidance work flow.

Acknowledgement. This research was supported by National Key Research and Development Program of China (No. 2017YFC0806500).

References 1. Graham-Rowe D (2010) Cheap drones could replace search-and-rescue helicopters. New Scientist 207(2769):20 2. Qin H, Cui JQ, Li J et al (2016) Design and implementation of an unmanned aerial vehicle for autonomous firefighting missions. In: IEEE international conference on control and automation. IEEE, Kathmandu, pp 62–67 3. Chakrabarty A, Morris R, Bouyssounouse X et al (2016) Autonomous indoor object tracking with the Parrot AR.Drone. In: International conference on unmanned aircraft systems. IEEE, Arlington, pp 25–30 4. Qiu Z, Zhang K, Dong Y (2017) Research on the system of patrol unmanned aerial vehicle (UAV) docking on charging pile based on autonomous identification and tracking. In: International conference on intelligent and interactive systems and applications. Springer, Cham, pp 517–523 5. Geng L, Zhang YF, Wang JJ et al (2013) Mission planning of autonomous UAVs for urban surveillance with evolutionary algorithms. In: IEEE international conference on control & automation, Chengdu, vol 45, no 5, pp 828–833 6. Doer C, Scholz G, Trommer GF (2017) Indoor laser-based SLAM for micro aerial vehicles. In: Gyroscopy & Navigation, vol 8, no 3, pp 181–189 (2017) 7. Li´enard J, Vogs A, Gatziolis D, Strigul N (2016) Embedded, real-time UAV control for improved, image-based 3D scene reconstruction. Measurement 81:264–269 8. L´ opez E, Barea R, G´ omez A, Nemra A (2016) Indoor SLAM for micro aerial vehicles using visual and laser sensor fusion. In: Robot 2015: second Iberian robotics conference. Advances in intelligent systems and computing, vol 417. Springer, Cham

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9. Mur-Artal R (2017) Tard´ os JD (2017) ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Trans Robot 33(5):1255–1262 10. Lv Q, Lin H, Wang G, Wei H, Wang Y (2017) ORB-SLAM-based tracing and 3D reconstruction for robot using Kinect 2.0. In: Control and Decision Conference, Chongqing 11. Beard RW, Mclain TW, Goodrich M, Anderson EP (2002) Coordinated target assignment and intercept for unmanned air vehicles. IEEE Trans Robot Autom 18(6):911–922 12. Mark H, Overmars Mark H, Overmars T (1992) A random approach to motion planning. RUU-CS (92-32) 13. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numerische Mathematik 1(1):269–271 14. Yan F, Liu YS, Xiao JZ, Yan F, Liu YS, Xiao JZ (2013) Path planning in complex 3D environments using a probabilistic roadmap method. Int J Autom Comput 10(6):525–533 15. V¨ or¨ os J (2000) A strategy for repetitive neighbor finding in octree representations. Image Vis Comput 18(14):1085–1091 16. Rodenberg O, Verbree E, Zlatanova S, Zlatanova S (2016) Indoor A* pathfinding through an octree representation of a point cloud. ISPRS Ann Photogram Remote Sens Spat Inf Sci IV-2/W1:249–255

Research on Port Throughput Prediction of Tianjin Port Based on PCA-SVR in the New Era Jinyu Wei1, Yuqiao Tang1(&), Yang Yu1, and Xueshan Sun2 1

2

College of Management, Tianjin University of Technology, Tianjin 300384, China [email protected] Central College of Information, Tianjin University of Technology, Tianjin 300380, China

Abstract. By selecting nine indicators related to Tianjin Port throughput in the new era, a PCA-SVR prediction model is constructed and the throughput of Tianjin port in 2017–2018 years is predicted. Based on the predicted results, the growth rate and influencing factors of Tianjin port throughput are analyzed. The research shows that the growth rate of Tianjin Port throughput has a downward trend in the next two years. In numerous influencing factors, the three indicators of the total value of imports and exports of foreign trade commodities, the added value of tertiary industry and Hebei port throughput accounts for the percentage of total throughput of Beijing-Tianjin-Hebei port have a significant impact on Tianjin Port throughput. Keywords: the Belt and Road Port through-put
PCA-SVR


Beijing-Tianjin-Hebei integration


1 Introduction For many years, the prediction of port throughput has been the focus of scholars in port research. Lu [1] established a dynamic penalty support vector regression model to study the influence of situational changes on port throughput. By dynamically adjusting the penalty coefficient of the data, the model can quickly adapt to the development law of things under the new situation and the prediction precision of the model is enhanced. And the annual cargo throughput data of Dalian Port and Tianjin Port are used to do empirical research. Duan [2] used NBS-SA software to analyze the impact of seasonal data on the throughput of the Bohai Rim port and to adjust it, which provides a new perspective for the study of port integration in the Bohai Sea. Yang [3] analyzed the effect of political and economic events and policy transformation on port throughput in China through historical data, finding the important impetus was political and economic element. Kang [4] studied the relationship between port network characteristics and cargo throughput, used network analysis to evaluate port network characteristics, and conducted panel regression analysis. The results show that throughput performance depends not only on macroeconomic variables and service capabilities, but also on the © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 45–52, 2020. https://doi.org/10.1007/978-981-32-9050-1_5

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central location of the port in the network. Chang [5] examined the impact of exchange rate movements, global economic activity, and the Baltic Dry Index (BDI) on cargo throughput fluctuations in Korean loading ports. Studies have shown that BDI volatility has a negative impact on cargo throughput, while nominal exchange rates and the growth of global economic activity have a positive impact. In order to investigate the impact of extreme wind events on port throughput, Zhang [6] used regression model to analyze it and conducted empirical analysis in Ningbo and Shanghai ports. Chen [7] quantitatively studied the relationship between Shanghai port throughput and Shanghai urban population growth rate by establishing an all-speed growth model between port throughput and port urban population. In summary, many scholars have done some relevant researches on port throughput prediction. However, the research on the combination of “Belt and Road” and the new era of Beijing-Tianjin-Hebei integration is scarce. Therefore, this paper selects nine indexes related to the throughput of Tianjin Port under the new situation. Based on the key influencing factors extracted by PCA, the SVR method is used to predict the throughput of Tianjin Port, and then to study the increasing trend of throughput, which provides a reference for the management of Tianjin Port in the future.

2 Research Method In order to eliminate the multiple collinearity between the index variables and improve the prediction precision, PCA-SVR model is used to predict the port through-put. The basic idea is to use PCA to extract the key impact factors affecting through-put, and then use it as an input variable to build an SVR model. Specific steps are as follows: (1) In order to eliminate the difference in the dimension of the indicator variables, the sample data is subjected to preconditioning standardization pre-processing; (2) Use PCA to reduce the dimensionality of the standardized data and extract key impact factors affecting port throughput; (3) Taking the extracted key impact factors as input variables, randomly select 65% of the samples from the sample data as the training set, and the remaining 35% of the samples as the test set, select the appropriate SVR kernel function and model parameters, and establish the SVR prediction model. 2.1

Principal Component Analysis (PCA)

Principal component analysis (PCA) is a commonly used data dimensionality reduction method, which can synthesize several variables into several representative variables, which is usually called principal component. There is no information redundancy between principal components, and most of the original information is included, thus simplifying complex problems.

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2.2

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Support Vector Regression (SVR)

The principle and method of SVR are as follows: for a given training sample fðxi ; yi Þjxi 2 Rn ; yi 2 R; i ¼ 1; 2; 3; . . .g, SVR uses the nonlinear mapping function UðxÞ to map the input data xi from n dimensional input space to the high-dimensional characteristic space, and conducts linear regression in the high-dimensional characteristic space to obtain the optimal decision function f ¼ x
Uðxi Þ þ b. Where x is the weight, b is the offset. Introducing insensitive function e, slack variable ni , and ni , SVR model solving can be transformed into the solution of the following planning problem: max

Wða; a Þ ¼ 12 þ

n P n P i¼1 j¼1 n P

ðai  ai Þðaj  aj ÞKðxi ; xj Þ

n P ðai  ai Þyi  e ðai þ ai Þ i¼18 i¼1 n X >  < ðai  ai Þ ¼ 0 s:t i¼1 > : 0  ai ; ai  C

ð1Þ

Among them, C is the penalty parameter, a; ai  0 is the Lagrangian multiplier,   Kxi ; xj ¼ /ðxi Þ
/ xj is the kernel function. This paper will select the Gaussian radial basis kernel function to construct the SVR model, and its expression is as follows: Kðx; zÞ ¼ expð

kx  z k2 Þ 2r2

ð2Þ

  Assuming the optimal solution for the problem is a1 ; a1 ; a2 ; a2 ; . . .; an ; an , the final support vector regression model is as follows: f ðxi Þ ¼

n X j¼1

ðai  ai ÞKðxi ; xj Þ þ b

ð3Þ

3 Outcome of Practice 3.1

Variable Selection and Data Source

From the angle of the construction of Tianjin Port itself, this paper selects the following indexes: the number of dock berths, Beijing-Tianjin-Hebei railway operating mileage, Beijing-Tianjin-Hebei road operating mileage, the primary industry value added, the secondary industry value added, the tertiary industry value added, the total GDP of Beijing-Tianjin-Hebei, the total value of foreign trade imports and exports and the percentage of port throughput of Hebei Province to the total throughput of BeijingTianjin-Hebei port. The datas mainly comes from the national data network, and some

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of the data comes from the Hebei Economic Yearbook, the Tianjin Statistical Yearbook and the Beijing Statistical Yearbook. 3.2

Data Standardization

Before using PCA to reduce the dimension of the selected index variables, in order to eliminate the difference in the dimension of the index variable and improve the prediction accuracy, the data should be pre-differentiated and standardized. The formula is as follows: xi0k

 i  xk  minðxi Þ  ð0:9  0:1Þ ¼ 0:1 þ ½maxðxi Þ  minðxi Þ

ð4Þ

Among them, xi0k indicates the value of the i indicator deviation normalization in the k year, x0k indicates the original value of the i indicator in the k year, and xi indicates the original value of the i indicator in all years. 3.3

Dimensionality Reduction by PCA

In order to quantitatively analyze the correlation of sample characteristics, this paper uses SPSS statistical analysis software to perform KMO test and Bartlett spherical test on sample data. The results are shown in Table 1. Table 1. KMO test and Bartlett spherical test results KMO test Spherical test Bangla Degree of freedom Significant

0.792 973.509 36 .000

It can be seen from Table 1 that the KMO test value reaches 0.792 (>0.5), and the Bartlett spherical test significance level is 0.000 ( > < > > :

1 q ¼ arctan yx22 y x1 ;

0 qd ¼ 2arctan xyc0x yc ;

c ¼ rqd ¼

2 2 1 ðxc x0 Þ þ ðyc y0 Þ xc x0 2

ð4Þ qd :

Then a new state vector is defined as   c f ¼ 2 R2 ; a ¼ q  qd : a

2.3

ð5Þ

System Kinematics

Without loss of generality, a camera-robot system can be developed by differentiating (5) with respect to time as follows:   d c @f f_ ¼ ¼ Jc Jr Ur ¼ Ji Jc Jr Ur ; dt a @pc

ð6Þ

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where  Ji ¼

l2 l1 l2 l1 l3 þ l5 l6  l4 l5  l3 l4 þ l6

 ð7Þ

the details of Ji refer to [10] and Jc 2 R46 is the image Jacobian or interaction matrix. Since the mobile robot moves on a plane, the motion of camera along z axis is not happened so that the height information of the feature points in F c maintains constant during the visual servoing task. The relationship can be represented as: Yic ¼ Yi ; i ¼ 1; 2;

ð8Þ

According to the geometric relation of the pinhole camera model with the perspective projection, the variables Z1c can be calculated as i

1 yi ; i ¼ 1; 2; c ¼ Zi fv Yic

ð9Þ

where fu , fv are the unknown focal lengths of the camera. According to the Assumption 2, there is Y  , Y1 ¼ Y2 . Then substituting (2), (7), (8), (9) to (6), the system kinematic model could be obtained: " f_ ¼

G5 F3

P4 i¼1

Gi F i

#

Ur ; P4 G5 F7 i¼1 Gi F4 þ i |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}

ð10Þ

H ðpc Þ

Where   1 a fu b 1 T G ¼ fu ; ;  ;  ;  2 R5 ; fu fv Y fv Y fv Y is an unknown constant vector about the camera parameters and three-dimensional scene information. Fi ; i ¼ 1


7 is the function about the feature points which can be calculated.

3 Control Strategy Design and Analysis The vision based set-point stabilization task is to converge the image radian error c and the state a to zero simultaneously. Meanwhile, the visual features should keep in the field of view during the regulation task of the wheeled mobile robot. Since these two states are defined as relative values with respect to the goal position, the system matrix H ðpc Þ will be singular at the goal position which is analyzed in [10]. This problem can be tackled by a switching control strategy to make the system states converging around the goal position. In addition, the inverse of the matrix H ðpc Þ cannot be directly

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calculated due to the unknown constant vector G. In order to achieve the control object, the controller should be designed to deal with the un-modeled uncertainty in H ðpc Þ and external disturbance. From the above analysis, a linear ESO is designed to estimate the uncertainties and compensate to the controller. Rewrite the system model (9) to a suitable form with the total disturbance as follows: " # P4 ^ 5 F3 ^ iFi G G i¼1 f_ ¼ Ur þ DðtÞ; ð11Þ P4 ^ i F4 þ i ^ 5 F7 G G i¼1 |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl ffl} ^ ðtÞ H

^ is a roughly estimated parameter vector, and H ^ is a function of the image where G points pc . DðtÞ denotes the total disturbance including the system uncertainty and external disturbance defined as   ^ ðtÞ Ur þ d ðtÞ: D ð t Þ ¼ H ð pc Þ  H

ð12Þ

Then a normal linear ESO is constructed for (10) to estimate the DðtÞ in real-time (

^ ðtÞUr  b1 ðZ1  f Þ; Z^1 ¼ Z2 þ H Z_ 2 ¼ b2 ðZ1  f Þ;

ð13Þ

where b1 , b2 are the positive parameters to be tuned such that the output Z2 of ESO can estimate the total disturbance DðtÞ. By using the estimation Z2 , the controller is designed as ^ 1 ðtÞðK  ðZ1  f  Þ  Z2 Þ: Ur ¼ H

ð14Þ

This control law will enable the system converge to the equilibrium point expo^ is singular at the goal position. To avoid this situanentially. However, the matrix H tion, a switchback strategy is adopted. If the midpoint of the current features ðxc ; yc Þ is close to the goal midpoint ðx0 ; y0 Þ with a nonzero image orientation error, a subgoal is  0 0   ^ 1 q; k2 y0 , where k1 [ 0, 0\k2 \1, will be used as the new chosen as x0 ; y0 ¼ k1 G reference in next iteration of the control loop. Then the switched subgoal will be judged in the same way until the system states converge around the goal position.

4 Simulations In this section, two groups of simulation are presented to validate the effectiveness and robustness of the proposed control strategy. A camera is fixed on the mobile robot and the intrinsic and extrinsic parameters are unknown. The desired posture is always set at origin, i.e. ðx ; y ; h Þ ¼ ð0m; 0m; 0 Þ. Then a pair of points in the world frame that satisfies the assumptions are selected as P1 ¼ ð0:1; 1; 2Þ, P1 ¼ ð0:1; 1; 2Þ. The virtual camera parameters are set as fu ¼ 672:18, fv ¼ 670:80, a ¼ 0:1, b ¼ 0:2. Then the

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parameters of the control strategy are tuned as b1 ¼ 0:8, b2 ¼ 0:16, K ¼ diag f0:4; 0:4g, k1 ¼ 0:3, k2 ¼ 0:75. The switching condition is set according to the midpoint error as jxc  x0 j\10ðpixelÞ, jyc  y0 j\10ðpixelÞ and the orientation error as jqj [ 0:05ðrad Þ. A random image noise with a maximum magnitude of 3 pixels is added to the system.

Fig. 3. Control performance and stabilization task with external disturbance.

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The initial posture of the mobile robot is set at ðx; y; hÞ ¼ ð2m; 1m; 30 Þ. An ^ is taken as estimated constant vector G  T ^ ¼ 650; 1 ; 0:15 ; 0:15; 1 G 650 650 650 for example to observe the control performance. Then large external disturbances are exerted on the states of the mobile robot to verify the robustness of this control strategy. The control parameters are not returned. We take the case with disturbances of ð1m; 0:5m; 5:7296 Þ for example. Figure 3(a) and (d) display the feature trajectories in image plane. These short segments are the lines connecting the two image points, and they are drawn at 50 sampling intervals. The circular points denote the features extracted at the initial pose and the star ones are the goal features. The image points move toward the goal features along an arc which pass though the midpoints of the goal features and current features. Therefore, the image trajectories always concentrate around the arcs which considerably decrease the possibility of missing features during the task. The motion path is shown in Fig. 3(b) and (e), the switchback is happened to avoid the singular position. It is obviously that a large slipping happened on the motion plane depicted in Fig. 3(e). After two switchbacks, the mobile robot arrives at the desired position. The control inputs of the two simulations are shown in Fig. 3(c) and (f). The red signal  on the curves denote the switching time. A pair  means one switchback motion of the mobile robot.

5 Conclusion A new approach to the visual servoing for wheeled mobile robot is provided with both uncertain camera parameters and unknown depth. The simplicity of the controller design in the ADRC framework is kept for the visual servoing of the wheeled mobile robot. Simulation results validate the proposed approach. Acknowledgment. The authors gratefully acknowledge the financial support of the National Natural Science Foundation of China (NSFC) under grants 61533012 and 91748120.

References 1. Li Z, Ying Z, Wen C (2017) Parking of nonholonomic mobile robots via switched control in the discrete time domain. In: IEEE international symposium on industrial electronics, Edinburgh, Scotland, pp 526–532 2. Mariottini GL, Oriolo G, Prattichizzo D (2007) Image-based visual servoing for nonholonomic mobile robots using epipolar geometry. IEEE Trans Rob 23(1):87–100 3. Lopez-Nicolas G, Gans NR, Bhattacharya S, Sagues C, Guerrero JJ, Hutchinson S (2010) Homography-based control scheme for mobile robots with nonholonomic and field-of-view constraints. IEEE Trans Syst Man Cybern Part B (Cybern) 40(4):1115–1127

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4. Huang Y, Su JB (2016) Simultaneous regulation of position and orientation for nonholonomic mobile robot. In: 2016 international conference on machine learning and cybernetics (ICMLC), vol 2, pp 477–482 5. Li B, Fang Y, Zhang X (2014) 2D trifocal tensor based visual servo regulation of nonholonomic mobile robots. Acta Automatica Sinica 40(12):5764–5769 (in Chinese) 6. Ito M, Hiratsuka T, Shibata M (2010) Feature-based visual target following for a nonholonomic wheeled mobile robot with a single camera. In: IECON 2010 - 36th annual conference on IEEE industrial electronics society, pp 2721–2726 7. Zhang X, Fang Y, Liu X (2010) Adaptive visual servo regulation of mobile robots. Control Theory Appl 27(9):1123–1130 (in Chinese) 8. Zhang X, Fang Y, Li B, Wang J (2017) Visual servoing of nonholonomic mobile robots with uncalibrated camera-to-robot parameters. IEEE Trans Industr Electron 64(1):390–400 9. Han J (1998) Active disturbance rejection controller and its application. Control Decis 13 (1):19–23 (in Chinese) 10. Hashimoto K, Noritsugu T (1997) Visual servoing of nonholonomic cart. In: Proceedings in IEEE international conference on robotics and automation, vol 2, pp 1719–1724

A Game Model for Gomoku Based on Deep Learning and Monte Carlo Tree Search Xiali Li, Shuai He, Licheng Wu(&), Daiyao Chen, and Yue Zhao School of Information Engineering, Minzu University of China, Beijing 100081, China [email protected] Abstract. Alpha Zero has made remarkable achievements in Go, Chess and Japanese Chess without human knowledge. Generally, the hardware resources have much influence on the effect of model training significantly. It is important to study game model that do not rely excessively on high-performance computing capabilities. In view of this, by referring to the methods used in AlphaGo Zero, this paper studies the model applying deep learning (DL) and monte carlo tree search (MCTS) with a simple deep neural network (DNN) structure on the Game of Gomoku Model, without considering human expert knowledge. Additionally, an improved method to accelerate MCTS search is proposed on the base of the characteristics of Gomoku. Experiments show that this model can improve the chess power in a short training time with limited hardware resources. Keywords: Game theory
Gomoku
Deep learning
Monte Carlo tree search

1 Introduction DNN [1, 2] and MCTS [3–5] can achieve superhuman performance even without supervision on human gameplay datasets. The emergence of DeepMind’s AlphaGo [6], AlphaGo Zero [7], and Alpha Zero [8] in Go, Chess, and Japanese Chess are remarkable, but for the majority of the research community, the training performance of the deep learning model is not obvious due to limited hardware resources. Before the advent of practical deep learning, classical search methods enjoyed initial success. The Methods used in Gomoku include Alpha-Beta pruning algorithm [9], Temporal-Difference algorithm [10], Markov decision process and Bayesian equalization algorithm. The Alpha-Beta pruning algorithm is a recursive tree search algorithm based on the Minimax algorithm [11] combined with the pruning method, and the evaluation method uses static situation assessment, relies on a large amount of expert knowledge to support the opening library [12], and the complex design of the valuation function. The system based on Temporal-Difference algorithm is fiercely over-reliant on human knowledge. The Markov decision process is based on the value iterative method and the strategy iterative method, so the strength is limited by the iterative method. The Bayesian equalization algorithm is based on scoring different patterns of the chessboard, and then accumulating the scores of all patterns, but it is impossible to systematically and comprehensively evaluate. Therefore, this paper studies the combination of the improved MCTS and DNN in the case of limited hardware resources, to reproduce, study, and extend AlphaGO Zero on © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 88–97, 2020. https://doi.org/10.1007/978-981-32-9050-1_10

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Gomoku game model. The model consists of three main parts: rules design, simple DNN design, and the improved MCTS. The rules include the basic rules of Gomoku and the rule of forbidden hands, although these forbidden rules reduce the training speed of the model, they can effectively solve the problem of “first-hand win” in Gomoku. The DNN is used for the evaluation of the board, where the value networks are responsible for a comprehensive assessment of the board’s score, while the policy networks are responsible for the prediction of the optimal move. The DNN guides the MCTS to realize the process of self-training to continuously improve the strength. Based on the Gomoku rules, we improve the MCTS process. The work in this paper is as follows: Firstly, do not rely on human knowledge and experience to improve strength. The model uses only the simple rules of Gomoku and the forbidden moves rules that are necessary to satisfy the model’s self-play process. Secondly, improved MCTS is presented to simplify the move strategy. This paper improves MCTS according to the basic rules and forbidden moves rules of Gomoku. Finally, DNN is applied to evaluate board positions and select moves. DNN are constructed by using a certain number of residual layers [13], combining the mature Tensorflow [14] and Keras [15] technologies, so that the whole model can gain a greater strength in a relatively short time with only one GPU. The rest of the paper is organized as follows. Section 2 introduces a simple DNN structure of Gomoku model. Section 3 deals with the improved MCTS. Test and analysis are presented in Sect. 4. Finally, the conclusion is summarized in Sect. 5.

2 The Design of Simple DNN Structure In order to reduce the depth of the neural network, in the first part of the whole neural network, we concentrate and merge the two branches of the value network and the policy network, and share the residual layers, from the second to fifth layers. Although the prediction accuracy of the neural network is slightly reduced, the process of model training is accelerated [7]. Starting from the sixth layers, it is divided into two branches, as shown in Fig. 1.

Input … …

Plain block

Residual

Policy head

Value head Fig. 1. The simple DNN structure.

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The networks trained here take as input a representation of the current position and output a probability distribution over all chess on the board, which are interpreted as a probability distribution over the possible places an expert player could place a chess. In order to prevent parameters from overfitting, the regularization term imposed on weights is partly accomplished by the method provided by Keras, which prevents overfitting by applying the regularizer function to the kernel weights matrix [16].

3 MCTS in DNN and Its Improvements 3.1

MCTS in DNN

The edge of the Monte Carlo tree stores the information obtained by each simulation [6, 7], and the information stored on each edge ðs; aÞ is as follows with the prior probability Pðs; aÞ, the mean action-value Qðs; aÞ and the visit count Nðs; aÞ, total actionvalue Wðs; aÞ. The process of MCTS in DNN, divided into the following steps: select, expand and evaluate, backup. 3.1.1 Select First of all, we assume that the data mentioned above already exist in the tree edge. In fact, we use these data to treat each position as a multi-armed bandit problem, and use UCT [17, 18] algorithm to select a max upper confidence bound Qðs; aÞ þ Uðs; aÞ, where pffiffiffiffiffi Na U(s; a) ¼ Cput  ðð1  epsilonÞ  Pðs; aÞ þ epsilon  nu½idxÞ  1 þ Nðs; aÞ

ð1Þ

Cpuct is a constant determining the level of exploration, epsilon is the proportion of noise interference, and nu½idx means Dirichlet noise, by adjusting Cpuct, epsilon and nu½idx, UCT algorithm can be as diverse as possible in the process of selecting action. And then, Na is the sum of visits of all nodes on a sequence with a as the root node. And 1

Pðs; aÞ ¼ Natan

ð2Þ

where tau is a parameter controlling temperature, when the total number of chess is less than the threshold, tau tends to be 1, ensure a diverse choose, otherwise, tau tends to be 0, to choose a maximum visit count move. This search control strategy initially prefers actions with high prior probability and low visit count, but asymptotically prefers actions with high action value. In the meanwhile, Pðs; aÞ is normalized. 3.1.2 Expand and Evaluate The selection process would then proceed until you reach a position where not all of the child positions have statistics recorded. The second phase, expansion, occurs when you

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can no longer apply UCT. At this time, the current state s of the board is passed as an input to the neural network, through policy networks, to provide placeholder prior probabilities for action selection Pðajs0 Þ, a is a legal move, which requires basic rules and forbidden rules to determine it’s legitimacy, and a is the set of A, s0 is the successor state. In the meanwhile, value networks gets a score for the current state s, VðsÞ. when chose one legal move a from A as the next simulation move a0 , there will be two choices: (1) When tau ! 0, select maximum visit count(highest probability) move a as a0 . If there are several move with the highest probability, then randomly select one of them. (2) When tau ! 1, a move will be randomly selected from the multinomial distribution. When we get a0 , a new record node a0 is added to the tree of statistics, the successor state s0 is added to the search tree. 3.1.3 Backup Backup occurs in two situations (stop the simulation and select a recommending move a to play). when the simulation reaches a leaf node at count MCTS SIMS, where MCTS SIMS is the upper limit of simulation times, and then, we will get Vðs; aÞ 2 ð1; 1Þ. Or when the playout reaches the end of the game, we will get Vðs; aÞ ¼ 1 or  1. According to value networks, we get the value of current state Vðs; aÞ after take action a, and one path L from root to a is also determined. For all nodes that at ðt 2 LÞ, the following values are updated: (1) Nt ¼ Nt þ 1, For a new node, the value of N is initialized to 1. (2) Wt ¼ Wt þ Vðs; aÞ  ðdirectionÞt , where ðdirectionÞt ¼ 1 or  1 Qt ¼ Wt =Nt As more and more playouts are run, the tree of statistics grows in memory and the move that will finally be chosen will converge towards the actual optimal play. 3.2

Improvements of MCTS

For AlphaGo Zero, a single training run requiring millions of self-play games needs days of training on thousands of TPUs, compared to Go, although the model of Gomoku is simpler, when we have only one GPU for training, the time consumed is quite large. Based on the characteristics of Gomoku, this paper proposes the following improvements. As shown in Fig. 2, When in state s, assuming that there is already a “connectfour”, value networks will give a very high score VðsÞ, and VðsÞ will not be equal to 1 or −1, but close to 1 or −1. At the same time, policy networks will also give the best move that predicted based on the current state P, and then, in the simulation process, a0 will be selected as the best move for the next step. If the simulation ends, backup, we will take a0 as the next move, s ! s0 . As mentioned earlier in the paper, in the process of model training, only when the evaluation result of value networks is equal to 1 or −1, or the simulation reaches a leaf node at count MCTS SIMS, then will backup. we

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1

1

3

1

2

3

......

1

...... Networks P

V =1 P

3

Backup Continue

V =1 Backup

s

...... 3

...... Networks

Basic rule

V =0~1

2

P

V =1 P

V =0~1

Backup Continue s’

Fig. 2. Improvements of MCTS.

know that VðsÞ is not equal to 1 or −1, and for the next state s0 , can be sure that the Vðs0 Þ equal to 1 or −1, but we need to call the DNN again to complete the process of VðsÞ ! Vðs0 Þ. The simulation process (choose a0 ) occurs before the DNN is called(get Vðs0 Þ), so this paper makes the following changes: in the simulation process, when choose a move a0 , call the basic rules to determine whether this move can form “connect-five”. If so, return directly to Vðs0 Þ ¼ 1 or  1, skipping the operation of the next value networks and policy networks; otherwise, it will work normally. In Fig. 3, we cite some of these situations, in which the winning or losing can be completely determined, so there is no need to continue with the subsequent simulations. Further research shows that there are many similar situations in the model of Gomoku, so these states can be added to the basic rules of judging the win.

Fig. 3. The winning board of Gomoku (a) black chess place at 9 (b) black chess place at 11

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Through such a change, the training process is greatly accelerated. The reasons why it is possible and can be accelerated are as follows: Compared with Go, judgment of winning or losing depends on all the chess on the board, in Gomoku, focus more on the outcome of the latest move, so when we get the next best move a0 by policy networks, we can already judge whether form “connect-five”, win or not, and it is not necessary to judge by value networks. And then When the MCTS SIMS is relatively high, most of the cases that trigger backup is Vðs0 Þ ¼ 1 or  1, so, we can foresee that a complete self-play process can skip a lot of time for running DNN. In the meanwhile, the score Vðs0 Þ that return directly is consistent with the score Vðs0 Þ returned by value networks, so it is guaranteed that the score will not be ambiguous can be used by MCTS. Finally, the rules for judging win belong to the category of basic rules.

4 Test and Analysis This paper verifies and analyzes some aspects involved in the Gomoku model, including the comparison of the model’s strength, the analysis of the model error, the comparison of the moving speed before and after the improvement of the MCTS, and the learning process of the model in training. 4.1

Result of Competitions

In order to observe the changes of strength, we use Keas to save the parameters obtained from self-play. After one-week training, 53 different versions of the network model are collected. Table 1 selects 27 of them, lets them play against with other version 10 times. Take the number 1 as an example, 1 vs 2, which means that black chess is the first version and white chess is the second version, 5:0:5 shows that in 10 confrontations, black chess wins 5 times, white chess wins 5 times and draw 0 times. From the number of 1–5, we can see that chess strength has obviously improved, and white chess is better than black chess. In order to observe the restrictions of the forbidden rules for the “first-hand win” advantage in self-play, in the numbers 6–16, we let black side uses the later version and white side uses earlier version, it is found that the forbidden rules will limit the chess power of black at the beginning, but as the training version increases, black side’s “first-hand win” advantage gradually becomes obvious, finally, this advantage exceeds the limit of the forbidden rules. From the number of 17–21, black side uses earlier version, the later version of Gomoku model achieves better performance than the earlier version, but in fact, the “first-hand win” advantage of black side weakens this “better performance”. Although “first-hand win” has a great impact on the self-play process, overall, the strength of the model has been improving, we used the last version against the first version for 100 times, and the last version had obvious advantages.

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Number Version Result Number Version Result Number Version Result

4.2

1 1 vs 2 3:0:7 9 10 vs 9 5:0:5 17 18 vs 19 3:0:7

2 2 vs 3 5:0:5 10 11 vs 10 6:1:3 18 19 vs 20 4:1:5

3 3 vs 4 3:0:7 11 12 vs 11 8:0:2 19 20 vs 21 4:0:6

4 4 vs 5 4:0:6 12 14 vs 13 5:0:5 20 21 vs 22 5:1:4

5 5 vs 6 1:0:9 13 15 vs 14 7:1:2 21 22 vs 23 6:0:4

6 7 vs 6 5:0:5 14 16 vs 15 9:0:1 22 1 vs 53 0 : 1 : 99

7 8 vs 7 4:1:5 15 17 vs 16 5:2:3 23 53 vs 1 100 : 0 :

8 9 vs 8 6:1:3 16 18 vs 17 5:1:4

0

Model Error

The target of using DNN to train model is to minimize the error between the actual winner and the value networks’ predicted winner, and to maximize the similarity between the actual movement and the policy networks’ predicted movement, Fig. 4 records the training errors of the value network and the policy network during self-play.

Train policy loss

Train overall loss

Train value loss

Fig. 4. Model’s training loss varies with the number of self-play.

The top line is the error in the policy networks, which is calculated by the mean square error, the bottom line is the error in the value networks, which is calculated by the cross entropy. The middle line is the average of both errors. In the beginning, the

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model loss is relatively large, because the Gomoku model has no experience for selecting a move, most of them are randomly selected. After more training, some experience is stored. The move is relatively reasonable and the error is reduced. And then, the error of the model decreases slowly, which also shows that the improvement of strength is slower than before. 4.3

Moving Speed

When DNN is used to guide MCTS, we speed up the moving time in training by changing the time of calling the basic rules. In 9 * 9 board, every step uses an MCTS with 100 simulations to select each move ðMCTS SIMS ¼ 100Þ. In the 1600 self-play games, the average time of the first 26 steps was recorded, and Gomoku model plays under no time limit. The average moving time before and after improvement is shown in Fig. 5. It can be seen that the speed of the move increases as the number of chess increases. This is because the search space of the legal moves decreases as the number of chess on the board increases, in the meanwhile, the thinking time also decreases. Besides, when the number is odd, the time is longer, in which the odd number is black and the even number is white. The black side needs to consider complex rules of forbidden moves, so the time of thinking will be longer.

Fig. 5. The mean time of each move.

4.4

Learning Process

The knowledge learned by the model is increasing, and a more stable opening formula is gradually adopted, the randomness of moving is gradually reduced, more moves prefer to be effective ones, and some common techniques used by human players are also learned to help expand the advantages of the situation, as shown in Fig. 6.

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Fig. 6. (a) In the 70 self-play games, Gomoku model has learnt to form double three: 3–11–7, 12–10–8. (b) In the 150 self-play games, learn to prevent the enemy from forming “connect-five” and “connect-four”: 11–7–13–5, 4–16–2. (c) At around 70 games, learn to expand advantage without triggering the forbidden moves rules, and there is “block-three-three”, and no forbidden rules are triggered: 4–3–11–5, 11–7–9–10.

5 Conclusion For the study of the game mode of Gomoku, this paper uses the tree search evaluated positions and selected moves using DNN. It only requires the basic rules and some forbidden rules of Gomoku, and doesn’t require expert knowledge. According to the characteristics of Gomoku, it is appropriate to change the search strategy of MCTS and improve the speed of the move. Tensorflow and Keras are used to build the neural networks, the training speed of the Gomoku model is guaranteed with only one GPU and single thread. After 1600 self-play games, the chess strength of the model has been greatly improved. Although it can’t achieve superhuman performance, it provides new insights for the ancient game of Gomoku, and provides a possibility to reproduce, study, improve upon, and extend AlphaGO Zero. Acknowledgment. This work is funded by National Natural Science Foundation of China (61602539, 61873291 and 61773416).

References 1. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444 2. Mnih V et al (2015) Human-level control through deep reinforcement learning. Nature 518 (7540):529–533 3. Bouzy B, Helmstetter B (2004) Monte-Carlo go developments. In: ACG, pp 159–174 4. Bouzy B (2006) Move-pruning techniques for Monte-Carlo go. In: Advances in computer games, pp 104–119 5. Guo X et al (2014) Deep learning for real-time atari game play using offline Monte-Carlo tree search planning. Adv. Neural Inf. Process. Syst. 4(27):3338–3346 6. Silver D et al (2016) Mastering the game of go with deep neural networks and tree search. Nature 529(7587):484–489

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7. Silver D et al (2017) Mastering the game of go without human knowledge. Nature 550 (7676):354–359 8. Silver D et al (2018) A general reinforcement learning algorithm that masters chess, shogi, and go through self-play. Science 362(6419):1140–1144 9. Papadopoulos A et al (2012) Exploring optimization strategies in board game abalone for alpha-beta search. In: 2012 IEEE Conference on Computational Intelligence and Games (CIG), pp 63–70 10. Barto AG (2007) Temporal-difference learning. Scholarpedia 2(11):1604 11. Hoki K, Kaneko T (2014) Large-scale optimization for evaluation functions with minimax search. J Artif Intell Res 49(1):527–568 12. Mnih V et al (2013) Playing atari with deep reinforcement learning. ArXiv Preprint arXiv: 1312.5602 13. He K et al (2016) Identity mappings in deep residual networks. In: European Conference on Computer Vision, pp 630–645 14. Abadi M et al (2016) TensorFlow: a system for large-scale machine learning. In: Operating systems design and implementation, pp 265–283 15. Choi K et al (2017) Kapre: on-GPU audio preprocessing layers for a quick implementation of deep neural network models with keras. ArXiv Preprint arXiv:1706.05781 16. Jain P et al (2010) Inductive regularized learning of kernel functions. Adv Neural Info Process Syst 23:946–954 17. Wang Y, Gelly S (2007) Modifications of UCT and sequence-like simulations for montecarlo go. In: 2007 IEEE Symposium on Computational Intelligence and Games, pp 175–182 18. Kocsis L, Szepesvári C (2006) Bandit based Monte-Carlo planning. European conference on machine learning, Springer, Heidelberg

Cascading Failure Analysis of Military Command and Control Network Based on SIS Model Jichao Xing1(&), Zhaoliang Zhu1, Chuxiang Chen2, and Xiaofeng Guo2 1

Graduate School, Information Engineering University, Zhengzhou 450001, China [email protected] 2 Department of Information Warfare Command, Information Engineering University, Zhengzhou 450001, China

Abstract. As the “central nervous system” of joint operations system, the military command and control network has become a key factor for the effectiveness of the system. Based on the characteristics of military command and control network, this paper proposes a networked command and control model based on the traditional hierarchical command and control model and study the problem of cascading failure on the network according to the classic infectious disease SIS model. The simulation and experiment of random failure and important node failure are carried out to verify the validity and usability of the model, which provides a new idea and method for subsequent research. Keywords: Military command and control network Cascading failure


SIS model


1 Introduction Cascading failure is the failure of one or a few nodes or edges in a complex network triggering the failure of other nodes owing to the coupling relationship among the nodes, which eventually leads to the collapse of a considerable number of nodes or even the entire network. Due to the emergence of complex networks, once a part of it fails, it is possible to amplify the impact of the fault through the connections of nodes, which ultimately has catastrophic consequences for the entire network [1]. Since 2002, Motter and Lai proposed the cascading failure theory analysis model for the first time, and commenced the study of cascading failure on scale-free network [2]. Researchers have proposed different cascading failure models for different networks such as power transmission networks, transportation networks, Internet, military networks, infectious disease cascades and so on. With the development of information technology, the current form of warfare is gradually transforming into a military system that is supported by information systems and combined with various combat forces such as land, sea, air, space, and cyberspace, which lays stress on integrity and emergence [3, 4]. As the “central nervous system” of © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 98–105, 2020. https://doi.org/10.1007/978-981-32-9050-1_11

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joint operations system under the conditions of informationization, the command and control (abbreviated below as C2) network plays a vital role in obtaining battlefield dominant control, supporting the system framework and enhancing operational efficiency. When the key nodes in the network are invalidated, it is highly probable that cascading failure will eventually result in deconstruction of the entire combat system. Therefore, the study of cascading failures in C2 network is an effective way to explore the immanent mechanism of warfare complex system and enhance the survivability and robustness of combat system. At present, the study of failure of military networks (such as C2 networks or communication networks) is mainly based on percolation theory [5–7]. However, the model assumes that node failure is an irreversible process. In other word, once a node fails, it will not be restored, which deviates from the actual situation of the combat operation. Therefore, this paper attempts to propose a new analysis method of cascading failure problem in military C2 network by means of the SIS (SusceptibleInfectious-Susceptible) model, which may provide reference for subsequent research.

2 Military Command and Control Network Modeling Through the use of various combat forces, the C2 network integrates the battlefield distribution’s combat power to maximize the combat space situation and achieve unified planning of operational resources [8]. Therefore, the military C2 network can be described as G = (V, E), nodes set V = {v1, v2, v3, …, vn} represents all levels and types of command agencies, edges set E = {e1, e2, e3, …, em} represents the command and control relationship command agencies. Referring to [9], the C2 network model can be divided into the traditional hierarchical C2 model and the networked C2 model. 2.1

The Traditional Hierarchical Command and Control Model

According to the traditional military system, the C2 network can be described as a tree network model in which “the first-level node commands and controls multiple secondary-level nodes, and multiple secondary-level nodes respectively command and control multiple lower-level nodes”, as shown in Fig. 1.

Fig. 1. Schematic diagram of the traditional hierarchical C2 model

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Therefore, the traditional hierarchical C2 model can be generated according to the following algorithm: Step 1. Determine the level L of the C2 network, the command and control span M of each node (the number of lower nodes commanded and controlled). Step 2. Generate a No. 1 level node (root node) and set the level flag Lindex = 1. Step 3. According to the command and control span M determined in Step 1, M subordinate nodes are generated and connected to the upper nodes respectively, Lindex = Lindex + 1. Step 4. Repeat Step 3 until Lindex = L. 2.2

The New Networked Command and Control Model

The new networked C2 structure is an improvement of the traditional hierarchical C2 model to maximize the interconnection efficiency among the C2 nodes. The model is formed on the basis of the traditional hierarchical C2 model by increasing the synergy between peers and the level of command relationship between cross-level nodes, as shown in Fig. 2.

Fig. 2. Schematic diagram of the new networked C2 model

The generation algorithm can be modified by adding the following steps based on the traditional hierarchical C2 model generation algorithm: Step 5. Determine the probability of synergy connection rc and the probability of cross-layer connection rs. Step 6. According to the level flag Lindex = 2, find a node set and add edges between two nodes according to the probability of synergy connection rc, Lindex = Lindex + 1. Step 7. Repeat Step 6 until Lindex = L. Step 8. An upper-level node set can be found according to the level flag Lindex = 1 and a lower-level nodes set can be found according to the level flag Lindex = Lindex + 2, Lindex = Lindex + 3,…, Lindex = L. Edges shall be added between two nodes according to the probability of cross-level connection rs, Lindex =Lindex + 1. Step 9: Repeat Step 8 until Lindex = L.

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Model Instance

Referencing paper [9], set L = 3, M = 8, rc = 0.1, rs = 0.05 and a new networked C2 model is generated according to the new networked C2 model generation algorithm. Table 1 shows the basic parameters of the network. Table 1. Basic network parameters of the new networked C2 model Number of nodes N 73

Number of edges E 560

Average degree 7.6712

Network diameter D 4

Average path length 2.2915

Clustering coefficient C 0.1130

3 Cascade Failure Analysis Based on SIS Model Both the SIR(Susceptible-Infective-Removal) model and the SIS model are classic infectious disease models, which were proposed by Kermack and McKendrick in 1926 and 1931 [10, 11]. A node of state I (Infective) in the SIS model can propagate the disease to a node of state S (Susceptible). At the same time, the node state will be changed from I to S, which means that the node is back to normal but resistless and may still be affected by other I nodes. In the actual combat process, the cascading failure process in the C2 network is like the SIS model. On one hand, C2 nodes may become ineffective due to internal or external factors and affect other nodes causing certain cascading failures; on the other hand, the failed node can be restored to normal state through certain repair work, but it does not mean that it has produced “antibody”. 3.1

Basic State Description of Nodes

The model is described as: In the military C2 network, owing to enemy damage or internal damage of the system, the nodes in the node set Xini are destroyed and affect other nodes in the form of probability propagation. Therefore, the node has the following two states. Normal State: The state in which the nodes operate normally and may be affected by other nodes. Because the importance of the nodes in the C2 network is different, the propagation factor ai is different when the nodes fail. Failure State: In the C2 network, a node fails due to external or internal factors. It has the possibility of transmitting the failure state to other alleged nodes, and at the same time, the failed node can be transformed into a normal state by self-repair. Generally, its recovery factor is determined by the node’s own attribute bi . 3.2

Propagation Factor and Recovery Factor

The values of the propagation factor ai and the recovery factor bi of the nodes in the C2 network reflect the diversity of the protection capabilities and propagation capabilities. Important nodes tend to have larger propagation factors and small recovery factor,

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which is difficult to restore to a normal state effectively once a failure phenomenon occurs owing to their own complicated structures. On the other hand, the general node’s propagation ability is not strong, which can be restored in time once the failure occurs because of its relatively simple system. In general, there are many factors that influence the importance of the node, such as location, node level, device performance, etc. In this paper, we selected degree centrality and betweenness centrality to calculate the propagation factor and recovery factor of nodes for the convenience of model simplification and experimental simulation according to reference paper [12, 13]. Degree centrality and betweenness centrality are parameters used to reflect the relative importance of each node in the network. The degree of centrality is defined as: the degree centrality Ci of node vi is the degree ki divided by the maximum possible degree N − 1, i.e. CD ðvi Þ ¼ ki =ðN  1Þ

ð1Þ

The betweenness centrality is defined as: the normalized betweenness of nodes vi, i.e. Bi ¼

X

½njl ðiÞ=njl 

ð2Þ

1  j  l  N;j6¼i6¼l

CB ðvi Þ ¼ 2Bi =½ðN  1ÞðN  2Þ

ð3Þ

In which, Bi is the betweenness of nodes vi, CB(vi) is the value of the betweenness centrality of node vi; njl is the shortest path between nodes vj and vl, njl(i) is the shortest path between nodes vj and vl through node vi, and N is the total number of nodes in the network. The calculation methods for the propagates factor and recovery factor of the node vi are respectively set as: 0

0

ai ¼ ð1  hÞekðCD ðvi Þ1Þ þ hekðCB ðvi Þ1Þ 1

1

0

0

bi ¼ ð1  hÞeuCD ðvi Þ þ heuCB ðvi Þ 1

1

ð4Þ ð5Þ

In which, CD0 ðvi Þ is the normalized value of the degree centrality of the node vi, is the normalized value of the betweenness centrality of node vi, h 2 ð0; 1Þ is a weight parameter, and k and u are adjustment parameters.

CB0 ðvi Þ

3.3

Propagation Process

It is assumed that the C2 network is attacked by enemy or fails to complete the command and control task for its own sake. The failed node i 2 Xini will affect the normal state nodes connected to it which result in the blocking or overloaded and eventually lead to its failure. In other word, the failed node will propagates the failure

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state to the nodes connected to it. Event A is that the failed node i propagates its failure state to the node j connected to it is A, then: Pi;j ðAÞ ¼ ai

ð6Þ

Under the influence of the failed node, other nodes will also gradually generate a failure state. Meanwhile, the failed node can be restored to its normal state by repairing itself. Event B is that the failed node j recovers from the failed state to the normal state is B, then: Pj ðBÞ ¼ bj

ð7Þ

As the process progresses, the C2 network will eventually exhibit a state of dynamic equilibrium. According to the analysis, the simulation steps of the propagation process are as follows: Step 1. Calculating the degree centrality and betweenness centrality of each node in the C2 network. Step 2. Calculate the propagation factor and recovery factor of each node according to formula (4) and (5). Step 3. Set the initial failed node set Xini according to the experimental requirements and update the failed node set XFai . Step 4. Judge whether the failed node returns to the normal state according to formula (3) and (7), and if it is restored, removing the nodes from XFai . Step 5. The failed node set A has an influence on other nodes. Judging whether a node j in the C2 network is also invalid according to formula (6), and if so, classify it into B. Step 6. Repeat Step 4 and Step 5 until the number of iterations is reached.

4 Simulation Results and Analysis According to the analysis, set the weight parameter h ¼ 0:5, adjustment parameters k ¼ u ¼ 1, the initial number of failed nodes is 7, and the number of simulation iterations is 50. Simulation experiments were carried out on two different cases of random failure and important node failure of the C2 network built in Sect. 2.3. The random failure is to randomly select 7 initial failure nodes from the network, reflecting the evolution of the nodes when the network is against random attacks. And the important node failure is to select 7 out of the 9 important nodes among level flag Lindex  2 as the initial failure node, reflecting the evolution of the node when the network is against the enemy’s precision attack. At the same time, in order to eliminate the random error that may occur in the simulation experiment, 50 experiments were repeated, and the overall results were analyzed. The results are shown in Fig. 3. As the figure shows, the ratio of normal nodes decreased at the beginning, when the C2 network suffers from random failure attacks. And because the nodes have repair capabilities, they can timely deal with node failures and eventually make normal node

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ratio in the network stabilize at around 90%, which basically eliminates the impact. When the C2 network is attacked by precision attack, the cascading failure problem of the network can affect more than half of the nodes in a short time. Ultimately, it is difficult to rely on the repair capability of the network itself to eliminate the impact of the cascading failure problem.

Fig. 3. Random node failure and important node failure simulation result curve

Simulating our network in the model constructed in paper [12] with the same initial parameters. The result that the node survival rate is 0.876 under the random attack and is 0.523 under the precision attack is basically consistent with this paper. Therefore, the C2 network model constructed in this parer has certain validity, and its “robust and fragile” characteristics are in line with the characteristics of the command and control system [4]. Based on the experiments, set parameter u ¼ 1; u ¼ 1:1; u ¼ 1:2; u ¼ 1:3 and simulate the important node failure problem of the C2 network. The results are shown in Fig. 4.

Fig. 4. Simulation results of important node failures under different parameter u

The result shows that, as the recovery ability of important nodes increases gradually, the impact of the C2 network cascading failure can be alleviated to some extent. However, due to the influence of important nodes, even if the recovery capability of important nodes increases, it is still impossible to avoid the phenomenon that the network performance declines more seriously in the early stage of failure. Therefore, how to accurately identify

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the important nodes in the C2 network and enhance the anti-destructive performance of important nodes are the key to improving the survivability of C2 network.

5 Conclusion The military C2 network is the key system in the future information warfare. Based on the characteristics of C2 network under the conditions of informationization, this paper establishes a new networked C2 model with traditional hierarchical C2 model. And simulating the cascading failure problem on the C2 network based on SIS model provides a way of addressing such problems. The shortcoming is that the parameter setting of the propagation and recovery capability is relatively simple, and the influence of defense capability and fault tolerance of nodes is not considered. In the next step, relevant in-depth research will be carried out.

References 1. Lao S, Wang J, Bai L (2016) Review of the interdependent networks. J Natl Univ Defense Technol 38(01):122–128 (in Chinese) 2. Motter AE, Lai YC (2002) Cascade-based attacks on complex networks. Phys Rev E 66 (8):065102 (S1539–3755) 3. Shen S, Zhang G, Zhu J (2012) Combat complex system modeling and experiment. National Defense Industry Press, Beijing (in Chinese) 4. Hu X, Yang J, Si G, Zhang M (2008) Simulation analysis and experiment of war complex system. National Defense Industry Press, Beijing (in Chinese) 5. Zhu T, Chang G, Zhang S, Guo R (2010) Research on model of cascading failure in command and control based on complex networks. J Syst Simul 22(08):1817–1820 (in Chinese) 6. Sun Z, Zhang B, Wang Q (2014) A cascade failure model based on war networks. J Lanzhou Univ Technol 40(05):95–99 (in Chinese) 7. Li K, He Y, Wu W, Liu F (2018) Reliability of complex hierarchical network for cascading failure. J Huazhong Univ Sci Technol (Nat Sci Ed) 46(09):45–51 (in Chinese) 8. Peng J, Zhang M (2016) Study of complexity, modeling and simulation of network information systems of system. J Equipment Acad 27(06):106–111 (in Chinese) 9. Luo J, Mao X, Liu Y, Wang C (2018) Research on modeling and evaluation of combat command system network structure. J CAEIT 13(06):642–649 (in Chinese) 10. Kermack WO, McKendrick AG (1927) Contributions to the mathematical theory of epdemics. Proc Roy Soc A115:700–721 11. Kermack WO, McKendrick AG (1932) Contributions to the mathematical theory of epdemics. Proc. Roy. Soc. A138:55–83 12. Shen D, Ren Q, Wang P, Zhu R (2018) Research and simulation of risk propagation model of military multiple elements state cyberspace. Syst Eng Electron 41(02):365–371 (in Chinese) 13. Kitsak M, Gallos LK, Havlin S et al (2010) Identification of influential spreaders in complex networks. Nat Phys 6(11):888–893

Laser Scan Matching in Polar Coordinates Using Gaussian Process Yinqiang Wang, Bo Li, Bo Han(&), Yu Zhang, and Wenjie Zhao Zhejiang University, Hangzhou, China {wangyinqiang,11224012,bhan1,zhangyu80, zhaowenjie8}@zju.edu.cn

Abstract. Laser scan matching serves an important role as pose estimation in autonomous navigation for mobile robots. As a classic approach, iterative closest point (ICP) finds corresponding point pairs in a brute-force way, which is time consuming. For eliminating the cost of searching the correspondence between the point pairs, polar scan matching (PSM) uses a matching bearing rule by making use of the laser measurements collected in polar coordinates. However, PSM might occur mismatching when the irrelevant region of the reference and current scans has a similar distribution on polar range. In order to obtain better results, we propose a novel laser scan matching method based on a new type of map representation using GP regression in polar coordinates. With this map representation, the corresponding point pairs can be found in a simple and efficient way. Based on these pairs, we get the robot pose by iteratively estimating the orientation and translation. For dense-point-scan data sets, our approach demonstrates an outstanding performance compared with traditional approaches like ICP and PSM in terms of both accuracy and efficiency. Keywords: Laser scan matching


Gaussian process
Polar coordinates

1 Introduction Laser scan matching plays an essential role in mobile robots navigation. Given two sets of laser range measurements, scan matching finds the rotation and translation that best align them. According to different types of sensors, laser scan matching can be classified into 2D and 3D situations. A 2D laser scan is a two-dimensional point set of noisy range measurements along with their incremented angles. This paper focuses on 2D laser scan matching. In the past, the problem of 2D laser scan matching has been well studied and there exists many approaches. Iterative closest point (ICP) [1] is widely used as a classic point-to-point matching approach. As its name suggests, ICP iteratively finds the closest points as corresponding pairs. Due to the closest principle, two scans have to be traversed for determining correspondence. This brings increased complexity and runtime consumption. In order to eliminate the cost of the search for point pair correspondence, polar scan matching (PSM) is proposed by Diosi [2]. PSM creates a matching bearing rule by making full use of the laser measurements in polar coordinates, where the polar radiuses can be associated with each other based on same © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 106–115, 2020. https://doi.org/10.1007/978-981-32-9050-1_12

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azimuth. PSM, however, might bring mismatching problem when the irrelevant region of the reference and current scans has a similar distribution on polar range. In pursuance of better results, we propose a novel laser scan matching method based on a Gaussian process (GP) regression in polar coordinates. First, we present a new representation of scan based on the Gaussian process map reconstruction method. This new representation is compact and can inherit the spatial structure of the raw laser scans. Based on this representation, we build a simple and efficient matching rule, which sets point pairs at same bearings. The pose is obtained by iteratively carrying out the orientation estimation and translation estimation. The work is a variation of our previous work [3], which used GP in Cartesian coordinates. The major contributions of this work are as follows, (1) A new representation of scan is proposed. Laser scans are reconstructed based on Gaussian process regression, which fully describes the spatial structure of the environments. This new representation can effectively avoid getting stuck in wrong local minima in orientation estimation. (2) A new matching rule is proposed, which can reduce the computational cost of transformation estimation. The structure of our paper is organized as follows: Sect. 2 describes the proposed GP-based laser scan matching approach. Section 3 presents the experiment results, where our approach is compared with ICP and PSM. Finally, Sect. 4 summarizes the proposed work.

2 Approach 2.1

Gaussian Process Map Reconstruction

In our work, GP is used for processing laser scans as a map reconstruction. There are two goals of GP map reconstruction: one is to convert the raw sensor into a lowerdimensional form to avert getting trapped in the wrong local minima in the scan matching process; the other is to adjust the new test locations into the desired locations, which is a fundamental step for finding the point pair correspondence. With these two goals, our approach can be regarded as a type of interpolation. We formulate the interpolation as a GP regression by taking the raw data as training points and using the test locations and predictions as the map reconstruction results. The procedures below are directly based on the work of Rasmussen [4]. The nonlinear regression problem is usually formed as follows: given a training data set T ¼ fli ; zi gni¼1 of n pairs of locations li 2 R and noisy observations zi 2 R, obtain the predictive distribution for the realization at the test locations li , denoted by zi ¼ f ðli Þ. In the typical GP regression,
a joint Gaussian distribution with average  function mðli Þ and covariance function k li ; lj is considered to model the data set. In addition,
we that the covariance on every observation is given  assume 2 cov ¼ k li ; lj þ rn , where r2n represents the variance of an independent and normally

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distributed noise term. Defining l ¼ ½l1 ; l2 ; . . .; ln , z ¼ ½z1 ; z2 ; . . .; zn , l ¼ ½l1 ; l2 ; . . .; lm  and z ¼ ½z1 ; z2 ; . . .; zm , the joint distribution is 

   kðl; lÞ þ r2n I z  N mðlÞ; z kðl; l ÞT

kðl; l Þ k ðl  ; l  Þ

 ;

ð1Þ

Where mðlÞ is the mean value of l, I is the identity matrix, kðl; lÞ is the matrix which is the so-called kernel function, and k ðl; l Þ, ðl ; l Þ is similar. In our work, we choose the exponential kernel as the covariance function to cope with more general environments. The exponential kernel function is given as  
 k li ; lj ¼ expðrf li  lj Þ;

ð2Þ

where rf is a length-scale. Given the joint distribution of Gaussian variables, the predictive value z at the test locations l is

1 z ¼ k ðl; l ÞT k ðl; lÞ þ r2n I z;

ð3Þ

and its uncertainty is provided by the covariance function

1 covðz Þ ¼ kðl ; l Þ  kðl; l ÞT k ðl; lÞ þ r2n I kðl; l Þ:

ð4Þ

In the context of the problem at hand, each location l is equal to / and each observation z is equal to q, where / and q are the bearing (azimuth angle) and the polar radius in polar coordinates, respectively. Involving calculating the inverse matrix of kðl; lÞ, the computational speed of the scan matching would dramatically slow down when the amount of the used data set is enormous. To deal with this issue, we use a fast approximation approach by utilizing the local information from the neighboring training points near each test bearing. Here, we choose the kd-tree as our nearest neighbor search tool. First, we create a kd-tree that arranges on the training points, which are stored by their distribution on bearings. By performing a kd-tree search, we find p training points for each test bearing. Then the prediction at this test bearing is computed using these training points. Based on the characteristic of the exponential kernel, the interrelationship would become weak when the distance between points is large. Therefore, this local approximation method could guarantee the accuracy while reducing the computational complexity. By querying the test bearings, the predictive points along with their variances are calculated using GP regression. Ambiguous predictions with large variance must be removed to obtain an accurate map. We use an example of typical laser scan data to illustrate the removing step of our map reconstruction method in Fig. 1. The predictive points with their variances are denoted by as scatter plots in Fig. 1(a). By selecting predictive points with variance below a certain threshold (like r \ 0:1), an accurate point-set-like map is obtained as shown in Fig. 1(b).

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Fig. 1. Illustration of the prediction selection procedure in Cartesian coordinate.

Back to the two goals introduced at the beginning of this sub-section, these goals can be achieved by customizing the number and values of the test bearings. By customizing the number of test bearings, the dimension of the map representation is reduced and the problem of getting stuck in wrong local minima is avoided. Customizing the values of the test bearings ensures that the corresponding relationship between the predictive point pairs used for scan matching is fixed, which avoids the expensive search for finding the point pair correspondence. 2.2

Iterative GP Points Scan Matching

Following an approach similar to that of PSM, we proceed with an iterative scan matching strategy and represent the rigid transformation by orientation estimation and translation estimation, separately. Assume that there are two scans in polar coordinates
 shown in Fig. 2(a): R ¼ xr ; yr ; hr ; f/ri ; qri gni¼1 is the reference scan, where xr ; yr ; hr describe the pose including position and orientation, and f/ri ; qri gni¼1 describe n laser range measurements qri at bearings /ri , similarly for the current scan C ¼ ðxc ; yc ; hc ; f/ci ; qci gni¼1 Þ. The pose variation ðDx; Dy; DhÞ describe the transformation between these two scans.

Fig. 2. Illustration of the iterative GP points scan matching using two typical laser scans.

The change of orientation from the reference scan to the current scan is easy to estimate in polar coordinates by a shift along with the azimuth axis. The correct orientation estimation can be found when the current scan covers the reference scan as

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much as possible. The translation can be estimated more accurately if the orientation has been estimated correctly. Thus, we aim to find the correct orientation estimation Dh first. In every shift, the average absolute range residual function r ðhÞ of the corresponding point pairs is calculated to measurement the size of the overlap region. Different from PSM that uses raw points for finding the best orientation estimation, our method uses the predictive points resulted from the GP map reconstruction step. And the corresponding point pairs are determined by the predictive points pairs R ¼ m f/ri ; qri gm i¼1 and C ¼ f/ci ; qci gi¼1 (see Fig. 2(b)) with the same test bearings and variances below the same threshold. The obtained orientation estimation ^h may not be accurate enough since the shifting is stochastic in a discrete form. The orientation correction ^ hc can be estimated using quadratic polynomial interpolation, which approximates the curve r ðhÞ consisted of 3 smallest residual points in the shifting step. Suppose that the 3 points are ð0; r0 Þ, ð1; r1 Þ and ð1; r1 Þ. According to the quadratic formula, the abscissa of the minimum rmin is ^hm ¼

r1  r1 : 2ðr1 þ r1  2r0 Þ

ð5Þ

In this example, we have assumed that the initial orientation estimation corresponds to 0 and the interval between points is 1. In general, the complete orientation estimation is actually Dh ¼ ^h þ ^hc ¼ ^h þ ^hm ures ;

ð6Þ

where ures is the angular resolution of the laser range finder. For every bearing /ri in polar coordinates, there always exists a polar range qri from the reference scan corresponding to a polar range qci from the current scan. In this part, we aim to find the translation estimation ðDx; DyÞ, which minimizes the P 0 weighted square range residual function J ¼ wi ðqri  qci Þ2 . As seen in this function, wi denotes a weighting factor that is employed to weaken the bad influence of mismatching. To minimize the objective function J, the Taylor expansion is applied to 0 linearize the nonlinear term qci ðDx; DyÞ as follows 0

0

0

qci ðbÞ  qci ðb0 Þ þ rqci ðb0 ÞT ðb  b0 Þ;

ð7Þ

0

where b ¼ ½Dx; DyT , b0 represents the initial value, and rqci ðb0 ÞT has been derived from two terms as follows 0

@qci 1 2ðqci cosð/ri Þ þ DxÞ ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi @Dx 2 ðqci cosð/ri Þ þ DxÞ2 þ ðqci sinð/ri Þ þ DyÞ2 0

q cosð/ Þ ¼ ci 0 ri ¼ cosð/ri Þ: qci

ð8Þ

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0

@qci @Dy

¼ sinð/ri Þ is obtained in a similar way. Then, we have the linearized objective function: J ðbÞ ¼ ½Z  H ðb  b0 ÞT W ½Z  H ðb  b0 Þ;

ð9Þ



T 0 0 where Z ¼ qr1  qc1 ; qr2  qc2 ; . . . , W ¼ diagðw1 ; w2 ; . . .Þ, and H is a matrix like 2 H¼

0

@qc1 6 @Dx 6 @q0c2 4 @Dy






0

@qc1 @Dy 0 @qc2 @Dy






3

2 cosð/r1 Þ 7 7 ¼ 4 cosð/r2 Þ 5




3 sinð/r1 Þ sinð/r2 Þ 5:




ð10Þ

According to the solution equation for weighted least squares [5], the translation estimation b is calculated by minimizing the objective function shown in (9).
1 b ¼ b0 þ H T WH H T WZ:

ð11Þ

When a transformation is calculated in a given iteration, it becomes the initial transformation for the next iteration. The map construction process for both the current scan and the reference scan is repeated to obtain a better point correspondence. The iteration procedure continues until the termination condition is satisfied. The termination condition is set as the orientation and translation estimation are both below a certain threshold. As shown in Fig. 2(c), the result of the aligned predictive point pairs is obtained. The transformation Tfinal also aligns the raw laser scans with high accuracy, as shown in Fig. 2(d), which verifies the effectiveness of our approach. The overall scan matching procedures are summarized in Fig. 3.

3 Experiment Results We implement the proposed approach on data sets gathered by the authors at a workshop in Hangzhou, China with a Hokuyo UTM-30-LX-EW laser range finder. The sensor works with a scanning frequency of 25 Hz and it produces 1081 range measurements per scan. The approach runs on a standard PC with a MATLAB R2017b environment. We also implement the well-known PSM and ICP methods for benchmarking. The performances of algorithms are compared with regard to accuracy and computational time. The accuracy of the alignment results is measured qualitatively and quantitatively. Due to the ground truth is not available, we evaluate the accuracy of the alignment by comparing the mean squared error (MSE), which is utilized by Holy [6].

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Fig. 3. Procedures of the iterative GP points scan matching

To test the robustness thoroughly, we adopted the scenes with absolute variations of pose varying from small to large. The alignment results of different scenes by our approach and the other methods are shown in Figs. 4, 5 and 6. The scan data in Scene 1 was obtained at the center of the workshop. As shown in Fig. 4(a), the pose had a small change from the reference location to the current location, thus two laser scans have a similar structure. The alignment results for Scene 1 are good for all three scan matching methods as one can see in Fig. 4(b)–(d). Scene 2 is situated on the border of the workshop. As seen in Fig. 5, the results are quite good except for ICP. Scene 3 is situated in a more cluttered environment where the overlap region is smaller than that in Scene 1 and 2. For this scene, ICP still fails to provide an alignment result good enough while our approach and PSM perform well, as shown in Fig. 6. To quantitatively compare the approaches, the MSEs, the run time (ms) and the number of iterations are shown in Table 1. As mentioned before, the alignment accuracies of the scans are compared through the MSE. As can be seen in the table, our approach performs a better result in terms of accuracy and efficiency than PSM and ICP in all three scenes.

Table 1. Quantitative performance of the three approaches. Workshop Scene 1 Scene 2 Scene 3 MSE Time Iterations MSE Time Iterations MSE Time Iterations Ours 0.0183 23.5 5 0.0205 15.1 3 0.0341 38.2 8 PSM 0.0252 65.2 11 0.0268 56.2 12 0.0483 114 23 ICP 0.0180 44.6 38 0.0441 42.1 53 0.1068 53.4 78

To further illustrate the advantage of our approach in terms of accuracy, we build a map with 100 consecutive aligned scans as demonstrated in Fig. 7. As shown in the figure, our approach and PSM form a more distinguished and clear structure of the environment, which outperform the result obtained by ICP. The MSE sequences of all methods are shown in Fig. 8. The average MSE of our approach, PSM and ICP are 0.0488, 0.0584, and 0.1307, respectively. And the variance of them are 0.0003, 0.0012, and 0.0024, respectively.

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Fig. 4 (a) Two unaligned scans in Scene 1. (b)–(d) The alignment result obtained by using our approach, PSM and ICP, respectively.

Fig. 5 (a) Two unaligned scans in Scene 2. (b)–(d) The alignment result obtained by using our approach, PSM and ICP, respectively.

Compared with PSM, the performance of ours is more stable, which can be observed from the variances of MSEs. This would make a great difference when it is being applicated in simultaneous localization and mapping (SLAM). Nowadays, SLAM becomes a more popular and notable issue of the robotics field [7]. The reason for the importance of stability is as follows: the pose estimation is a crucial step in the whole SLAM procedure. Once an incorrect pose estimation occurs in a single step, it may lead to the divergence of localization, often causing catastrophic failure of the SLAM algorithm. With similar accuracy, a pose estimation method with better stability is more desirable for SLAM method. Thus, our approach is more suitable for the state estimation in SLAM compared to the other two standard approaches.

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Fig. 6 (a) Two unaligned scans in Scene 3. (b)–(d) The alignment result obtained by using our approach, PSM and ICP, respectively.

Fig. 7 (a) Map result obtained by using our approach, PSM and ICP, respectively.

Fig. 8. The MSE sequences of three methods for each scan matching step.

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4 Conclusion In this paper, we proposed a novel laser scan matching approach based on a new type of map representation using GP regression. The lower-dimensional map representation can fully reveal the structural signature of the environment given by the raw laser scan. Based on this map representation, the point pair correspondence can be determined directly and the transformation can be estimated accurately. The experimental results demonstrate a better performance of our approach compared to the traditional ICP and PSM methods with regard to accuracy and efficiency for the dense-points-scan data set. Acknowledgements. This paper has been supported by the National Natural Science Foundation of China (Grant No. 61673341), the National Natural Science Foundation of China (Grant No. 61703366), the Fundamental Research Funds for the Central Universities (2016FZA4023, 2017QN81006), National Key R&D Program of China (2016YFD0200701–3), the Project of State Key Laboratory of Industrial Control Technology Zhejiang University (No. ICT1913) and the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (No. ICT1900312).

References 1. Besl P, McKay ND (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256 2. Diosi A, Kleeman L (2007) Fast laser scan matching using polar coordinates. Int J Robot Res 26(10):1125–1153 3. Li B, Wang Y, Zhang Y, Zhao W, Ruan J, Li P (2019) GP-SLAM: novel laser-based slam approach based on regionalized gaussian process map reconstruction (Manuscript submitted for Autonomous Robots on 14 Feb 2019, now under review) 4. Rasmussen CE, Williams CKI (2005) Gaussian processes for machine learning. The MIT Press, Cambridge 5. Kay SM (1993) Fundamentals of statistical signal processing: estimation theory. Prentice-Hall Inc., Upper Saddle River 6. Holý B (2016) Registration of lines in 2D lidar scans via functions of angles. IFACPapersOnLine 49(5):109–114 7. Cadena C, Carlone L, Carrillo H, Latif Y, Scaramuzza D, Neira J, Reid I, Leonard JJ (2016) Past, present, and future of simultaneous localization and mapping: toward the robustperception age. IEEE Trans Robot 32(6):1309–1332

An Evolutionary Membrane Algorithm Based on Competition Mechanism for Multi-objective Optimization Problems Zhiqiang Geng, Yunfei Cui, and Yongming Han(&) Beijing University of Chemical Technology, Beijing 100029, China [email protected]

Abstract. The increasing focuses on coordinated developments of society, economy and environment makes multi-objective optimization an important tool for solving real-world problems. Thus an evolutionary membrane algorithm based on competition mechanism (EMACM) is proposed in this paper, which incorporates advantages of the NSGA-II evolution and the distributed structure of the membrane computing. The communication process distinguished the membrane algorithm with other intelligent algorithms. To share information between evolved populations, best objects selected are communicated to the upper-layer membrane through the competition mechanism to eliminate dominated solutions. The skin membrane archives global best objects as elitists, and serves as guidance for inner evolution processes. Verified by test functions, the EMACM is able to find global solutions that are converged well, approximated closely to and covering as much as possible the real Pareto front, and distributed uniformly along the whole front. Compared with classical algorithms, the EMACM demonstrates better performances of convergence and diversity. Keywords: Multi-objective optimization
Membrane computing Communication rule
Competition mechanism
Elitist archive




1 Introduction The worldwide focus on sustainable development has brought more and more attention to production and living optimization for rational resources employment [1]. Efficient optimization helps to raise efficiency, escaping from unnecessary waste in social and economic developments and protecting the environment as well. Coordinated optimization of multiple objectives becomes more and more realistic. Multi-objective optimization problem (MOP) is a heated topic in scientific research and engineering practice. In the previous work [2], we made a review about intelligent algorithms for solving MOPs and compromising methods for dealing with multiple objectives. Intelligent algorithms are able to optimize objectives without coupling them, maintaining individual features of each single objective. Membrane computing [3] was initiated by Păun in 1998 and thus abbreviated as P system. It is originated from the structure and function of living cells, as well as

© Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 116–123, 2020. https://doi.org/10.1007/978-981-32-9050-1_13

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interaction between cells, tissues and even neurons. The P system is mainly consisted of the membrane structure with membranes, objects in the membranes, and evolution and communication rules acted on objects. There are usually elementary membranes, middle membranes involving elementary membranes, and a skin membrane, arranged in hierarchical structure. All evolution rules work in a maximally parallel manner. In 2004, Nishida [4] firstly proposed the notion of membrane computing optimization algorithm (abbreviated as membrane algorithm). In the structure of a membrane algorithm, there are some objects as candidate solutions and an optimization algorithm as the evolution rule in each region. Objects communicate between regions. It has been proved that membrane algorithms have high reliability, fast convergence speed and good solutions. As a relatively new and promising intelligent method, an evolutionary membrane algorithm is proposed based on competition mechanism (EMACM), to improve exploration and exploitation abilities for finding global optimization solutions. The intrinsically parallel and distributed computation feature of membrane computing manifests its advantages in producing uniformly distributed solutions. The communication process based on the competition mechanism helps the algorithm converged to the real Pareto front.

2 Evolutionary Membrane Algorithm Based on Competition Mechanism An EMACM is proposed in this paper, extending application of the membrane algorithm and enhancing exploration and exploitation abilities for solving MOPs. The membrane structure is designed as a three-layer hierarchical cell-like P system. According to theorems and corollaries by Păun [3], a P system with at most 6 membranes is able to realize universal computation. Thus, we use 6 membranes in the middle-layer, each with two elementary membranes. (1) Initial objects for optimization problems are produced randomly in given decision variable ranges. On the basis of NSGA-II [5], evolution rules are used to update objects in membranes except the skin membrane. (2) Communication distinguishes the membrane algorithm with other intelligent algorithms. Membranes communicate their best objects in each iteration step to the directly upper membranes till the skin membrane. Objects to be communicated are selected based on crowding distance sorting [5]. The larger the crowding distance value, the more potential to be selected. Making use of Pareto dominance, dominated solutions are eliminated during the competing communication, as shown in Table 1.

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The skin membrane receives best objects communicated and acts as an archive storing global elitists. Meanwhile, the skin membrane also transmits global best objects back to inner membranes. The steps for implementing EMACM are summed up as the flowchart in Fig. 1.

Start

Setup: set up the membrane structure and algorithm parameters

Initialization: randomly produce initial objects in inner membranes

Is iteration time reached?

Selection

No

Crossover

Objects are evolved in each membrane

No

Mutation Are all objects evolved?

No

Yes

Crowding distance sorting

Communication

Competition

1.Objects are communicated to upper surrounding membranes 2.Skin membrane: elitists stored and objects are communicated back to inner membranes

Is iteration time reached?

Yes

Output elitist solutions in the skin membrane

End

Fig. 1. The flowchart of EMACM

Yes

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Table 1. The pseudo-code of communication based on competition mechanism

3 Test Experiments The EMACM is verified by the DTLZ test function set [6] with extendible decision variables and objectives (here we take three objectives). The algorithms’ performances of convergence, uniformity and distribution are demonstrated by evaluation indexes of the convergence metric IC [7], the spacing metric IS [7], and the generational distance IGD [7]. The verification experiments on the EMACM are carried out by computer with 2.40 GHz Intel Core 2, CPU E4600, 2.00G RAM, and WIN10 32 bit operating system. The algorithm parameters are set up as NE ¼ 12; NM ¼ 6, the number of SE ¼ 15, the number of SM ¼ 10; max iteration ¼ 20; generations ¼ 80, crossover rate ¼ 0:8; mutation rate ¼ 1=n, communication rate ¼ 30%. The worst time complexity of NSGA-II is equivalent to O(GmN2). In a parallel working mode, the worst time complexity of EMACM is O(GmNmax2) (Nmax denotes the maximal population size of membranes). For serial working mode, the complexity

is expressed as NE  O mS2E þ NM  O mS2M . As usual, there is ðNE  SE þ NM  SM Þ  N. Thus, the time complexity of EMACM in parallel mode is lower than that of NSGA-II while a little lower in serial mode. Numerical Simulation Results. The optimization performances of the EMACM are compared with results of some classical algorithms like the NSGA-II, the SPEA2 (strength Pareto evolutionary algorithm) the MOEA/D (multi-objective evolutionary algorithm based on decomposition), the SMG-MOMA (multi-objective membrane algorithm guided by the skin membrane) [8]. In general, it takes the average and standard deviation values of statistical results of evaluation indexes in 30 runs for performance

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Table 2. Performance comparison between EMACM and other algorithms (statistical results of 30 runs, Ic , mean(std)) (*the bold denotes the best result) Problems NSGA-II SPEA2 DTLZ1 3.66E−2 7.03E−2 (1.20E−3) (2.97E−1) DTLZ2 5.37E−2 2.34E−2 (1.74E−3) (1.31E−3) DTLZ3 3.30E−1 6.33E−1 (7.54E−1) (9.98E−1) DTLZ4 5.36E−2 2.32E−2 (1.95E−3) (4.64E−3) DTLZ7 6.97E−2 3.02E−2 (3.12E−3) (1.43E−3)

MOEA/D 1.54E−2 (1.68E−3) 1.91E−2 (3.18E−4) 3.82E−2 (1.34E−2) 3.26E−2 (3.33E−2) 1.24E−1 (1.86E−1)

SMG-MOMA 1.42E−2 (6.30E−4) 2.26E−2 (9.78E−4) 1.22E−1 (2.49E−1) 2.38E−2 (1.41E−3) 2.94E−2 (2.94E−2)

EMACM 6.87E−3 (3.42E−3) 1.16E−2 (6.44E−3) 1.09E−2 (5.31E−3) 3.26E−2 (1.54E−3) 7.59E−3 (1.75E−3)

Table 3. Performance comparison between EMACM and other algorithms (statistical results of 30 runs, Is , mean(std)) (*the bold denotes the best result) Problems NSGA-II SPEA2 DTLZ1 2.14E−2 2.88E−1 (2.08E−3) (1.51E+0) DTLZ2 5.73E−2 2.40E−2 (4.75E−3) (2.40E−2) DTLZ3 2.75E+0 3.92E+0 (7.72E+0) (6.22E+0) DTLZ4 5.82E−2 2.08E−2 (5.29E−3) (8.22E−3) DTLZ7 6.82E−2 3.26E−2 (8.72E−3) (4.90E−3)

MOEA/D 7.15E−3 (1.24E−2) 5.53E−2 (2.57E−3) 5.29E−2 (3.48E−3) 1.24E−1 (9.52E−2) 8.85E−1 (1.46E−0)

SMG-MOMA 7.67E−3 (7.59E−4) 2.29E−2 (1.69E−3) 8.38E−1 (2.06E+0) 2.37E−2 (1.64E−3) 3.08E−2 (2.82E−3)

EMACM 5.20E−5 (5.83E−5) 1.86E−4 (1.50E−4) 1.30E−4 (1.68E−4) 2.34E−4 (1.73E−4) 9.95E−5 (5.70E−5)

Table 4. Performance comparison between EMACM and other algorithms (statistical results of 30 runs, IGd , mean(std)) (*the bold denotes the best result) Problems NSGA-II SPEA2 DTLZ1 2.68E−2 1.95E−2 (1.65E−3) (2.43E−4) DTLZ2 7.08E−2 5.30E−2 (2.89E−3) (7.26E−4) DTLZ3 7.42E−2 5.42E−2 (5.55E−3) (2.17E−3) DTLZ4 6.97E−2 1.34E−1 (2.85E−3) (1.83E−1) DTLZ7 9.03E−2 6.73E−3 (5.79E−2) (5.05E−2)

MOEA/D 2.68E−2 (4.01E−) 4.90E−2 (1.91E−4) 6.15E−2 (1.02E−2) 2.18E−1 (2.65E−1) 4.93E−1 (2.71E−1)

SMG-MOMA 1.94E−2 (1.19E−4) 5.25E−2 (4.49E−4) 6.16E−2 (4.00E−2) 5.30E−2 (8.17E−4) 5.67E−2 (2.06E−3)

EMACM 1.88E−4 (1.09E−4) 3.46E−4 (1.96E−4) 2.25E−4 (1.42E−4) 9.14E−4 (3.73E−4) 1.69E−4 (9.24E−6)

verification, as shown in Tables 2, 3 and 4 for different optimization algorithms. Moreover, due to limited paper length, the approximation trends of the EMACM to real Pareto fronts of test functions DTLZ1-3 are demonstrated in Figs. 2, 3 and 4.

An EMACM for MOPs DTLZ1

0.8

Real Pareto front Approximate Pareto front

Function 3

0.6 0.4 0.2 0 0 0.8

0.2 0.6

0.4 0.6 0.8

Function 1

0.4

0.2 0

Function 2

Fig. 2. Approximation of EMACM to the real front on DTLZ1

DTLZ2

Real Pareto front Approximate Pareto front

Function 3

1.5

1

0.5

0 0 1.5

0.5

1

1

0.5 1.5

Function 1

0

Function 2

Fig. 3. Approximation of EMACM to the real front on DTLZ2

DTLZ3

Real Pareto front Approximate Pareto front

Function 3

1.5

1

0.5

0 1.5 0

1

0.5

0.5 Function 1

1 0

1.5

Function 2

Fig. 4. Approximation of EMACM to the real front on DTLZ3

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It can be seen from results that: (1) the statistical average and standard deviation values of IC of the EMACM are smaller than other algorithms for functions DTLZ1*4 and DTLZ7; (2) the values of IS of the EMACM are smaller than most algorithms; (3) it is almost the same for IGD as IS . As a whole, although the EMACM does not exceed other intelligent algorithms in all indexes, its resulting fronts are converged closely to the real Pareto fronts with uniform distribution, while covering the whole front, making it promising in solving MOPs.

4 Conclusion An EMACM is proposed in this paper for finding converged and well-distributed solutions that cover as many optima in the real Pareto front as possible of the MOP. Objects in each membrane are evolved independently without contradicting with other objects in other membranes. The selected best objects are communicated between upper and lower membranes to share information, based on the crowding distance sorting method. The communicated solutions are competed based on the Pareto dominance to sift out dominated solutions. Moreover, the skin membrane acts as an elitist archive for storing global best solutions and guidance for inner membranes evolution through communicating objects back. The proposed EMACM is tested in some famous test functions, and its performance indexes are compared with previous classical intelligent algorithms. The statistical results demonstrate good performances of convergence, uniformity and distribution of the EMACM in solving MOPs. The parallel computation and competing communication are able to produce considerable amount of solutions distributed evenly along the Pareto front as close as possible to the real front. If possible, as objects in each membrane of EMACM can be evolved independently, the computation efficiency of EMACM that is executed in parallel computing environment will be higher than that in serial mode. In the future, we will make further researches on real-world application and the many-objective optimization problem (MaOP) with more objectives. Acknowledgement. The work is partly funded by the National Key Research and Development Program of China (2017YFC1601800) and (XK1802-4).

References 1. The 2030 Agenda for Sustainable Development. United Nations, 25 September 2015 2. Cui Y, Geng Z, Zhu Q, Han Y (2017) Review: multi-objective optimization methods and application in energy saving. Energy 125:681–704 3. Păun G, Rozenberg G (2002) A guide to membrane computing. Theor Comput Sci 287:73– 100 4. Nishida TY (2006) Membrane algorithms. LNCS, vol 3850, pp 55–66 5. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197

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6. Deb K, Thiele L, Laumanns M, Zitzler E (2002) Scalable multi-objective optimization test problems. In: Proceedings of the IEEE Congress on Evolutionary Computation. IEEE Service Center, Piscataway, pp 825–830 7. Deb K, Jain S (2002) Running performance metrics for evolutionary multi-objective optimization. IEEE Trans Evol Comput 10:13–20 8. Li J (2016) Research on multi-objective optimization algorithm based on membrane computing models. University of Anhui, Hefei (in Chinese)

Design of Intelligent Measuring Device for On-line Machining Parts of Lathe Xiangyang Sun1,2(&), Binggao He1, Lijuan Shi1, Can Wang1, and Yuegang Fu2 1

2

Changchun University, Changchun 130022, China [email protected] Changchun University of Science and Technology, Changchun 130022, China

Abstract. To be higher the production efficiency of mechanical parts, after analyzing the shortcomings of conventional parts dimension measurement technology, a design scheme of automatic device which can realize on-line dimension measurement of parts by lathe is proposed. It integrates the work of lathe processing, dimension measurement and quality appraisal into one process innovatively. This scheme uses projection optical system to replace the traditional scanning scheme. The measuring device can not only complete the realtime and high-precision measurement of 15 parameters, but also ensure easy operation and maintenance. At the same time, the optical system design has a collimated beam expanding path, which makes the measuring device have a larger measurement range. Combining with the working environment of the measuring device, the design of the shock absorption mechanism is introduced in the structural design, so that the high stability of the measuring results can be maintained under the normal operation of the lathe. Finally, by extracting feature parameters and testing the matching interface, it is proved that the designed device fully meets the technical requirements proposed at the beginning of the study. Keywords: Part Measurement


Optics
Image Processing

1 Introduction The measurement technology of mechanical parts size is one of the main factors that restrict the processing level of mechanical parts. With the rapid improvement of the mechanical processing level, new requirements are constantly put forward for the measurement technology of the size of mechanical parts. Firstly, the production must have complete electronic information of production and quality testing. Secondly, the measurement of parts must meet the requirements of high precision, real-time and multi-parameter. Thirdly, the measuring device should be easy to operate and maintain. In this paper, a new measurement scheme for lathe processing is proposed. At the same time, the optical system, image processing system and mechanical structure of the device are analyzed and designed in detail [1].

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2 Design Scheme and Performance Index of Measuring Device 2.1

Design Scheme of Measuring Device

Considering the field environment (dust-free, vibration-free, noise-free) of lathe processing and the real-time requirement of parts measurement, a laser scanning scheme is proposed, namely optical projection technology. Its basic principle is to combine optical, image processing and computer algorithm to measure the size of mechanical parts. Its detection principle is shown in Fig. 1. High-definition camera is used to photograph the shadows of the parts under test, and computer image analysis is used to get the mechanical dimensions of the parts being processed. At the same time, by matching the characteristics of the standard parts, the final judgement is whether the parts being processed are qualified. This design scheme not only ensures the accuracy of the measured parts measurement, but also realizes the synchronization of parts lathe processing and measurement, which greatly improves the efficiency of the production and detection of mechanical parts [2]. Telecentric optical system

Collimator lens

Reflector

Reflector

Camera lens CMOS

Diffusing lens LED

Fig. 1. Basic principle based on projection optical measurement

2.2

Performance Indicators of Measuring Devices

The measurement system using this technology can achieve the following major performances, as shown in Table 1. Table 1. Main technical parameters No. 1 2 3 4 5

Key performances Measuring length range Measuring diameter range Measurement accuracy Measurement time Number of measured parameters

Performance index No limit u1 mm–u65 mm  ±2 lm 2 s 15

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3 Key Technology of Measuring Device 3.1

Optical System Design of Measuring Device

The total optical path of the system can be divided into front and back parts. The front optical path first completes the irradiation of the measured parts with high collimation parallel light, while the other optical path completes the convergence of high position accuracy of the shadow image of the measured parts. 3.1.1 Design and Analysis of Optical System Analyzing the actual situation of the parts processed by lathe, it can be found that the size of the parts processed by lathe is different, and the dimensions of different mechanical characteristics are different. Therefore, the distance between objects in the optical system will change constantly. After the distance changes, the image height changes at the same time, then the acquisition size of the parts tested will change, that is, the measurement error occurs in the detection. Similarly, even when the objects are measured, the measurement error will occur. If the distance remains unchanged, the measurement error will also be caused by the difficulty of completely coinciding the COMS sensitive surface with the image plane [3]. Therefore, the optical system design focuses on how to reduce the measurement error caused by the change of object distance and the accuracy of imaging position. Figure 2(a) is the object telecentric optical path. If the change of the measured part size causes the change of object distance or the object surface offset due to external vibration, the image plane moves from AB to A1B2. According to conjugate relation, the image received by the fixed image acquisition plane will be a diffuse image, but because the main light is parallel to optical axis, the change from object distance will not be created from the object space. It can be inferred that when the aperture diaphragm is placed on the image square focal plane, the image distance will change, but the image height will not change, even if the object distance changes. Therefore, the size of the image received by the CMOS image plane will not change, that is, the measured object size will not change L′1 = L1, which ensures the distance between the lens and the target even if the thermal expansion causes the movement of the photosensitive element. The image size will not change if the object surface is changed or the object surface is offset by vibration, so the measurement error will not be caused. Figure 2(b) is a image telecentric optical path, the aperture diaphragm is placed on the focal plane of the object. After the object passes through the pupil at the focal point, it is imaged at infinity. If the measured object does not coincide with the actual imaging position due to external vibration or the imaging receiving surface (COMS image plane), the image received by the receiving surface (COMS image plane) will be a diffuse speckle, but the main light of the image side of the optical path will still pass through A′1, B′1 two spots, and the actual height L′1 is equal to the height A1B2, that is L1 = L′1. It can be seen that the position error of receiving surface (COMS image plane) can be eliminated by using the image telecentric optical path, which is the measurement error caused by the change of image distance [4, 5]. Therefore, the technical advantages of these two optical paths will be combined in measurement.

Design of Intelligent Measuring Device for On-line Machining Parts of Lathe A

Aperture stop Entrance pupil

A1'

M A1

L1'=L'

L

Scaleplate

Aperture stop

B1

B1'

L1

B

M

B1'

A

A1

B'

L1'=L1

A'

B1

127

A1'

B

(a) Object telecentric optical path

(b) Image telecentric optical path

Fig. 2. Measurement principle of telecentric optical path

3.1.2 Design Results of Optical System In view of the design requirements of the required optical path for the above parts inspection, an optical system is designed using design software ZEMAX. The Fig. 3(a) is a general-purpose part detection optical path. The emitted beam of the right light source passes through a group of collimating lenses on the right side, forming a parallel beam to illuminate the detected part. The shadow image formed by the detected part is gathered by another group of collimating lenses on the left side and received by the sensor on the left side. In order to facilitate the installation of the detection system on the lathe processing station, one reflector is designed on the left and right of the optical path to shorten the one-dimensional size of the entire optical system, and the mechanical structure of the whole detection system is greatly reduced. Considering the dimension detection of lathe parts with high aspect ratio, a large dimension detection optical path with collimation and beam expanding performance is optimized on the basis of general detection optical system, such as Fig. 3(b), one side of larger size can be rotated through lathe and adjusted to the illumination surface of light path in inspecting, so that the measurement of larger size surface of parts can be completed.

Measured parts

(a) Non-collimated beam expansion

Beam expanding lens

(b) Collimated beam expansion

Fig. 3. Optical path diagram of parts inspection

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Structural Design of Measuring Device

The measuring device is specially designed for the detection of lathe parts. Its working environment, including vibration, shock and temperature change, will affect the characteristics and stability of optical system, and will interfere with the final measurement accuracy.

CMOS Lens Seismic focusing mechanism

CMOS

Mirror shock absorber (tension wire)

Lens

Seismic focusing mechanism Light source Light source Mirror shock absorber (tension wire)

Fig. 4. Structure of measuring device

Vibration and impact is a kind of working condition that cannot be completely eliminated. Vibration comes from the driving mechanism such as the motor inside the lathe, and also from the external vibration caused by the change of cutting force or other external force impact when processing parts. Because the external vibration can often be rapidly attenuated, it can be neglected. Therefore, in the design of the opticalmechanical structure of the measuring system, only the influence of the fixed vibration frequency is considered. Three design methods are used to weaken the impact or vibration of the working environment on the final accuracy when the lathe parts are measured, structure of measuring device is shown in Fig. 4. Firstly, the anti-vibration design of the mirror mechanism in the optical path is carried out, that is, the mirror base of the mirror adopts the tensioned wire structure, which can weaken the projection jitter of the measured part caused by the vibration in the process of vibration. Secondly, adjustable design of the lens base of the convergent lens in the optical system is carried out, which ensures that the shadow of the measured part can be accurately projected to the focal plane of CMOS camera during the vibration process of the measuring device by setting a driving motor mechanism. Finally, the PTFE shim is assembled on the connecting base of the measuring device and the lathe, which can not only ensure the stiffness of the base, but also play the role of vibration buffer.

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Image Acquisition and Processing of Measuring Device

The image acquisition and processing system of the measurement system is one of the key systems of the developed detector. It is mainly used to extract the image of parts and match the standard image. Shadow image acquisition Standard Parts Image

Successful initial matching?

Central Point Location of Characteristic Parameters

Classification K of characteristic parameters Region Extraction Algorithms Feature extraction algorithm

Feature pattern preprocessing

N

Y Feature pattern feature extraction Score Weight of Initial Matching and Schema Features in Class K Feature Database of Feature Images Successful initial matching

Feature matching algorithm Initial Matching Failure

Pattern feature matching of feature parameters

Class K feature database of feature image Matching failure

Second Successful Matching quadratic Failure matching

Matching success

Fig. 5. Extraction and matching process of image features

Considering the characteristics of shadow image feature extraction and matching, the image acquisition and processing system of this detector adopts three different image region extraction algorithms, feature extraction algorithms and feature matching algorithms to match different feature standard images. The process of collecting and processing part shadow image is shown in Fig. 5 [6, 7].

4 Testing of Measuring Device The designed measuring device is tested, and its display interface is shown in Fig. 6. Figure 6(a) is a parameter extraction interface for measuring parts, i.e. features of 15 parts. Figure 6(b) is a parameter matching interface for testing parts. If the measured parameters are within the accuracy range of standard parts, the parts tested are qualified.

(a) Non-collimated beam expansion

(b) Collimated beam expansion

Fig. 6. Optical path diagram of parts inspection

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As can be seen from the Fig. 6(a), the serial parameters can be chosen before the measurement of parts. Figure 6(b) shows that the measurement results of 15 parameters fully meet the requirements of standard parts.

5 Conclusion After analyzing the principle of the on-line inspection technology for lathe parts proposed in this paper, an optical system with collimation and beam expanding characteristics is designed according to the requirement of feature parameter image extraction and the characteristics of projection optical system design. Based on the design results of optical fiber system, the measuring conditions of lathe parts are analyzed, and the structural design with shock absorption characteristics is carried out. Finally, the detailed flow of part shadow image acquisition and processing is designed. Through the actual test, it is verified that the designed measuring device can fully meet the performance indicators set in this study. Acknowledgment. This research was supported by the Ministry of Education “Chunhui Project” (Grant No. Z2017031) and Science and Technology Project of Jilin Provincial Department of Education 2016 (Grant No. JJKH20180938KJ), all support is gratefully acknowledged.

References 1. Glauco VP (2013) Image feature descriptor based on shape salience points. Neurocomputing 120(23):156–163 (in Chinese) 2. Awrangjeb M, Lu GJ, Fraser CS (2012) Performance comparisons of contour-based corner detectors. IEEE Trans Image Process 21(9):4167–4180 (in Chinese) 3. Mao S, Zhao J (2019) Optimal design for multi-layer diffractive optical elements with antireflection films. Acta Opt Sinica 39(3):0305001-1–0305001-8 (in Chinese) 4. Guo J, Wang Z, Lu M (2019) Off-axis three-mirror anastigmatic system alignment and application based on principal component analysis. Acta Opt Sinica 39(3):0322002-1– 0322002-7 (in Chinese) 5. Xing Y (2019) Optimization design and test of the main structure of a mini-optical device in space. Infrared Laser Eng 47(11):1113002-1–1113002-7 (in Chinese) 6. Waghule DR, Ochawar RS (2014) Overview on edge detection methods. In: 2014 International conference on electronic systems, signal processing and computing technologies, pp 151–156 7. Zeng XZ, Lu DH (2008) A new method of camera calibration based on opencv and tsai. Mech Electr Eng Mag 25(12):49–52

Algorithm Research on Optimizing Ordering and Pricing Policy for Perishable Items Xin Yang1,2,3(&), Yuan Zhao1, Jin-yu Wei4, and Yang Yu4 1

2

Zhonghuan Information College Tianjin University of Technology, Tianjin 300380, China [email protected] School of Economics and Management, Hebei University of Technology, Tianjin 300401, China 3 Tianjin Hantuo Computer Technology Research Institute, Tianjin 300462, China 4 School of Management, Tianjin University of Technology, Tianjin 300384, China

Abstract. In reality, perishable products also contain defective ones before storage due to uncertainties during production process. In order to reduce the loss caused by the defective products, an inspection process is implemented by retailers. In view of the above, a retailer’s deteriorating inventory model with defective items is established in this article. Then GA and PSO are used to figure out the model respectively and the usefulness of the intelligent algorithm is confirmed by comparing the outcomes of the two algorithms. Keywords: Inventory strategy Intelligent algorithm


Perishable items
Defective products


1 Introduction As to decrease the risk of cargo damage and maintain the cost advantage of the supply chain, inventory models considering the deteriorating items have been increasingly concerned by many scholars in these years [1–7]. However, some assumptions of these deteriorating inventory models are idealistic and unrealistic in nature. With the realistic consideration in mind, several scholars studied the deteriorating production/inventory systems with imperfect items over the past decade [8–10]. In view of the above, a retailer’s deteriorating inventory model with defective items is established in this article. Then GA and PSO are used to figure out the model respectively and the usefulness of the intelligent algorithm is confirmed by comparing the outcomes of the two algorithms.

2 Formulation of the Inventory Model 2.1

Environment Setting and Notations

The notations used in the proposed model are as follows: Q: the replenishment quantity per order; x: the screening rate; hðtÞ: the deteriorating rate, hðtÞ ¼ abtb1 , where a is the scale parameter and b is the shape parameter, © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 131–137, 2020. https://doi.org/10.1007/978-981-32-9050-1_15

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a [ 0; b [ 1; D: the demand rate; Y: the defect rate that subjects to a probability distribution with the desired value E ðY Þ ¼ y; M1 : the possibility of Type I flaw (classifying an fine product as imperfect), the desired value is E ðM1 Þ ¼ m1 ; M2 : the possibility of Type II flaw (classifying a imperfect product as good), the desired value is E ðM2 Þ ¼ m2 ; c: the purchasing cost in each unit; O: the ordering cost in each order; h: the holding cost in each unit per unit time for good products (Unscreened products are regarded as good products.); hs : the holding cost in each unit per unit time for imperfect products; c1 : the cost of rejecting an perfect product; c2 : the cost of accepting a imperfect product; q: the screening cost coefficient; Decision variables: T: the replenishment cycle time; ts : the screening period; 2.2

The Mathematical Model

Once the products arrive at the warehouse, the retailer begins to screen all the nondeteriorated products until t ¼ ts . The inventory curve of good products is shown by the full line I ðtÞ in Fig. 1, and the inventory curve of defective products is shown in Fig. 2. By definition, although the actual defect rate of products is y, the defect rate detected by the retailer is yr ¼ ð1  yÞ
m1 þ y
ð1  m2 Þ due to the existence of two types of screening errors.

Fig. 1. The inventory curves of the good products and the returned products

Fig. 2. The inventory curve of the defective products

The Inventory of the Good Products The consumption and the screening process can be described as: dI ðtÞ ¼ hðtÞI ðtÞ  D  x
yr ; 0  t  ts dt

ð1Þ b

at The stocking level of the retailer could be expressed as:  I ðtÞ ¼ e ½Q þ ðD þ b 2 R 0 aub R0 ðau Þ x
yr Þ
t e du; 0  t  ts . Let AðtÞ ¼ t 1 þ aub þ 2! du ¼ t  b þa 1
tb þ 1  a2 2ð2b þ 1Þ

b


t2b þ 1 , then we get: I ðtÞ ¼ eat ½Q þ ðD þ x
yr Þ
AðtÞ; 0  t  ts . The variation of inventory is formulated as:

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133

dI ðtÞ ¼ hðtÞI ðtÞ  D dt

ð2Þ

b

Then we get the inventory level: I ðtÞ ¼ eat
D
½AðtÞ  AðT Þ; ts  t  T. In summary, the stocking level of fine goods at time t is:  I ðt Þ ¼

b

eat ½Q þ ðD þ x
yr Þ
AðtÞ; b eat
D
½AðtÞ  AðT Þ;

0  t  ts ts  t  T

ð3Þ

Then, the total inventory of good products during the time ½0; T  is: Z

ts

I¼ 0

Z I ðtÞdt þ Z

ts

¼Q
Z

ts

T

I ðtÞdt

b

eat dt þ ðD þ x
yr Þ


0 T

¼D
0

b

eat
AðtÞdt þ x
yr


Z

ts

0 Z ts

b

eat
AðtÞdt þ D


Z

T ts

b

eat
½AðtÞ  AðT Þdt

b

eat
AðtÞdt  D
AðT Þ


0

Z

T

b

eat dt  x
yr
Aðts Þ


0

Z

ts

b

eat dt

0

The Inventory of the Defective Products Screened It is easy to get: Is ðtÞ ¼ x
yr
t; ð0  t  ts Þ

ð4Þ

Then through the equation substitution, the optimal screening rate of the retailer is b Rt b T Þ
eats ab
Bðts Þ
D obtained: x ¼ D
Aðat , where Bðts Þ ¼ 0s tb1
eat
AðtÞdt. The total b ts þ yr
e s
Aðts Þ þ ab
yr
Bðts Þ Rt inventory of the defective products in ½0; T  is: Is ¼ 0s Is ðtÞdt ¼ 12 x
yr
ts2 . The Inventory of the Defective Products Returned The inventory of the returned products Ir ðtÞ is driven by both the deteriorating rate and the return rate, so: dIr ðtÞ ¼ m2
y
D  hðtÞIr ðtÞ dt

ð5Þ

Then the inventory of the returned products at time t is solved as: b

Ir ðtÞ ¼ m2
y
D
eat
AðtÞ; 0  t  T

ð6Þ

RT The total inventory of the returned products is Ir ¼ 0 Ir ðtÞdt ¼ m2
y
D
R T atb
AðtÞdt. Finally, the holding cost is HC ¼ h
I þ hs
ðIr þ Is Þ; The purchasing 0 e cost is CC ¼ Q
c; The deterioration cost is PC ¼ cðQ  D
T  x
yr
ts Þ þ c½m2
y
D
T  Ir ðT Þ. The Type I cost is MC1 ¼ c1
x
ð1  yÞ
ts
m1 ; The Type II cost is  2 MC2 ¼ c2
x
y
ts
m2 ; The screening cost is IC ¼ 12 q
Tts ; The expected cost per unit time is E ðpÞ ¼ T1 ½O þ EðHC Þ þ EðCC Þ þ E ðPC Þ þ EðMC1 Þ þ E ðMC2 Þ þ E ðIC Þ.

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Then the optimal decision variables of the proposed model can be obtained by the following objective function and the constraint conditions. min E ðpðT; ts ÞÞ 8 IðtÞ  0 > > > < xð 1  y Þ  D r s:t: > > > ts  Q=x : 0  ts =T  1

3 Optimization Method and Optimization Result This paper set the model parameters based on the relevant data of Hsu and Hsu [11]. The parameter values are shown in Table 1. 3.1

Genetic Algorithm (GA) and Its Optimal Results

After setting the algorithm parameters and the model parameters, the GA can be used to solve the model. From Table 2, after about 30 generations of optimization iterations, the GA has been stabilized. After the optimization reaches 300 generations, the GA has been converged. Then, the cost of each part of the unit time is plotted. For the effect of picture display, the purchase cost in per unit time is reduced by ten times, which does not affect the general trend of cost. The ultimate cost of each part in unit time is shown as in Fig. 4. In the process of GA optimization, except for the screening cost, the average cost in each part of the cost in each unit time illustrates a downward trend. The screening cost in per unit time demonstrates a trend of decreasing at first and then increasing during the optimization process, and it tends to be stable after 50 generations of the optimization algorithm. Finally, the optimal solution of the model obtained by GA is: T  ¼ 4, Q ¼ 605, ts ¼ 3, x ¼ 198:14. And the corresponding optimal cost is 4312.42 RMB. The above is the process of GA optimization for the proposed inventory model. Figure 3 shows the overall optimization trend of the objective function. The trend was found that after optimization iteration to 30 generations, the algorithm basically converged. However, Fig. 4 shows the trend of the cost of each part. It was found that the cost of each part showed a downward trend as a whole, and some showed an upward trend. 3.2

Particle Swarm Optimization (PSO) Algorithm and Its Optimization Results

In PSO, the parameters are set in Table 3 and then are solved iteratively. The optimization solution process is shown in Fig. 5. From Fig. 5, when iterating to the 10th generation, the algorithm has tended to converge, and it has completely converged when iterating to 300 generations. From this point, the PSO converges faster than the GA. And its optimization effect is basically similar to GA.

Algorithm Research on Optimizing Ordering and Pricing Policy Table 1. Model parameter value Notation D O h hs c c1 c2 q a b m1 m2 y

Value 137 units/day $50/order $0.012/unit/day $0.006/unit/day $25/order $50/unit $200/unit 500 0.01 1.05 0.04 0.04 0.04

Fig. 3. GA objective function trend graph

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Table 2. GA algorithm parameter value Parameter Population size Decision variable coding length Crossover probability Mutation probability The maximum number of iterations

Value 80 7 0.75 0.03 300

Fig. 4. Cost trend graph of various parts of GA

Next, the cost trend of each part in the PSO algorithm is displayed in Fig. 6. Same as the GA, since the purchase cost CC is much larger than the cost of other parts, here for the effect of the picture display, it is reduced by 10 times for display. Table 3. PSO algorithm parameter value Parameter Number of particles Acceleration constant c1 Acceleration constant c2 Inertia factor w Maximum number of iterations

Value 50 1.7 1.7 0.6 300

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Fig. 5. Total cost trend graph of PSO

Fig. 6. Cost trend graph of each part of PSO

Finally, the optimal replenishment cycle is T  ¼ 4; and optimal order quantity is Q ¼ 605. Optimal screening time is ts ¼ 3 and screening rate is x ¼ 197:28. Finally, the corresponding optimal cost is 4130.29 RMB. Because the computer itself has calculation inaccuracy of floating point number, the iterative error accumulation in the calculation process leads to a slight difference from the GA result, but it can be seen that the result is basically the same as GA. It can be seen from Fig. 6 that the trend of the cost of each part shows a downward trend as a whole, and some areas fluctuate up and down and eventually stabilize. Compared cost trends of various parts in the optimization process of GA, eventually we can find that, in the process of PSO and GA optimization, the highest of all costs is the first category of error cost except for purchase cost. The second highest is screening cost, followed by metamorphic cost, second category of error cost and storage cost. Therefore, retailers should focus on the occurrence of first category of error and avoid excessive cost increases. Also, on this basis, the screening cost and the cost of deterioration should be appropriately controlled. The above is the process of PSO for optimizing the proposed inventory model. Figure 5 shows the overall optimization trend of the objective function. From Fig. 5, it is clearly that the optimization iteration is about 10 generations, and the algorithm basically converges. However Fig. 6 shows the trend graph of the cost of each part, in which the cost of each part is generally declining, and some of them show upward and downward fluctuations. A comprehensive comparison between PSO and GA can be found that the results are basically the same. But PSO has a better convergence speed. 

4 Conclusion The characteristics of deterioration and imperfect quality for a single product are taken into account in the inventory system of a retailer. The deteriorating ratio of the goods is supposed to be a two-parameter Weibull function that is superior to the hypotheses of constant or linear function in previous researches. Meanwhile, considering the fact that product was deteriorating all the time, non-100% inspection is conducted by the retailer. This differentiates our research from others. In the inspection process, two types of screening flaws (Type I and Type II) are also considered in the inventory

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system, whose probabilities are random variables with known probability distribution. Then a mathematical model of joint replenishment, pricing and screening policy is established based on the above preconditions. The optimal solutions are obtained by GA and PSO with the objective of maximizing the expected profit per unit time. A comprehensive comparison between PSO and GA can be found that the results are basically the same. But PSO has a better convergence speed. Acknowledgments. The authors get support from the Tianjin Philosophy and Social Science Project (Grant Nos. TJGL18-042).

References 1. Chaudhary RR, Sharma V (2015) A model for Weibull deteriorate items with price dependent demand rate and inflation. Indian J Sci Technol 8(10):975–981 2. Mishra U (2016) An EOQ model with time dependent Weibull deterioration, quadratic demand and partial backlogging. Int J Appl Comput Math 2(4):545–563 3. Janssen L, Claus T, Sauer J (2016) Literature review of deteriorating inventory models by key topics from 2012 to 2015. Int J Prod Econ 182:86–112 4. Tat R, Taleizadeh AA, Esmaeili M (2015) Developing economic order quantity model for non-instantaneous deteriorating items in vendor-managed inventory (VMI) system. Int J Syst Sci 46(7):1257–1268 5. Dash BP, Singh T, Pattnayak H (2014) An inventory model for deteriorating items with exponential declining demand and time-varying holding cost. Am J Oper Res 4(01):1–10 6. Kumar S, Singh AK (2016) Optimal time policy for deteriorating items of two-warehouse inventory system with time and stock dependent demand and partial backlogging. Sādhanā 41(5):541–548 7. Pandey R, Singh S, Vaish B et al (2017) An EOQ model with quantity incentive strategy for deteriorating items and partial backlogging. Uncertain Supply Chain Manag 5(2):135–142 8. Alamri AA, Harris I, Syntetos AA (2016) Efficient inventory control for imperfect quality items. Eur J Oper Res 254(1):92–104 9. Pickard AS, Lin HW, Leon MD et al (2005) Impact of inspection errors on the performance measures of a general repeat inspection plan. Int J Prod Res 43(23):4945–4967 10. Chen TH (2017) Optimizing pricing, replenishment and rework decision for imperfect and deteriorating items in a manufacturer-retailer channel. Int J Prod Econ 183:539–550 11. Hsu JT, Hsu LF (2013) An EOQ model with imperfect quality items, inspection errors, shortage backordering, and sales returns. Int J Prod Econ 143(1):162–170

Facial Expression Recognition System Based on Deep Residual Fusion Neural Network Haonan Wang, Junhang Ding(&), Fan Wang, and Zhe Ma Qingdao University, Qingdao 266071, China [email protected]

Abstract. Rich and varied facial expressions are the intuitive carriers for transmitting emotional information to each other. Due to the variety of facial expressions, the extraction of features is quite difficult. The traditional manual extraction method can neither achieve better recognition accuracy nor guarantee the recognition efficiency. This paper uses 18-layer residual neural network, and realizes permanent mapping by means of the short-circuit connection of residual modules to ensure the network capability of deep structures. At the same time, the CLBP texture features are extracted, and the two are innovatively combined to form a more representative description feature. The experimental results show that compared with the DCNN, DBN and other networks, the convergence time is shorter and the average recognition rate is 93.24%, which is nearly 5% higher. Keywords: Deep residual Fusion neural network


Facial expression recognition


1 Introduction In addition to the language expression and physical movement, facial expression changes are also important emotional output media for information communication between people. Although the related algorithms of machine learning improve the quality of recognition, the generalization ability of the network is expected to be very limited [1]. The deep neural network has the self-learning property of fitting biological neurons, it extracts the abstract description features of the samples, and significantly improves the generalization ability of the network [2]. On this basis, the residual neural network [3] uses the residual module of the shortcut connection, which avoids the adverse interference on the test recognition rate along with the increase of the network structure depth.

2 Deep Residual Neural Network 2.1

Residual Network Advantage

In the research of deep convolutional neural networks, the ideal state is that the deeper of network structure, the more features can be learned, but it is often counterproductive

© Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 138–144, 2020. https://doi.org/10.1007/978-981-32-9050-1_16

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in the actual training test [4]. Imagine a large or small number that become very large or very small after repeated multiplying and exponential stacking. The extreme cases such as the gradient disappearance and the gradient explosion [5] are more pronounced along with the network deepens. The convolutional layer and the pooling layer in the ordinary flat neural network all have the effect of downsampling. Although it can avoid excessive fitting, it will inevitably lead to a large loss of information with potential to contribute. The residual neural network exhibits better performance in solving these problems. It designs a “short circuit” module [6], which connects the original input to the output after the first two layers, and simply superimposes the output after the convolution of the two layers. In this way, the computation of network parameters is not significantly increased, and the gradient problem caused by the information loss can be effectively avoided [7]. In addition, the network capacity is improved while the network depth is increased. 2.2

Residual Module Implementation

There is no simple linear relationship between the network depth and recognition accuracy. The accuracy will reach a maximum at a certain network depth, and then will decrease with the increase of depth [8]. If the recognition accuracy of the shallow flat network reaches a peak and the subsequent layer introduces a congruent map, the recognition accuracy will not be adversely affected by the deepening of the network layer. The residual module maps the original input of the first few layers of the network to the output layer through a shortcut connection. If the original input is x and the ideal output is H(x), the target output after the introduction of the residual module is converted to F(x) = H(x) − x [9]. In other words, the goal of network learning is transformed from the original complete H(x) to a relatively simple residual H(x) − x between output and input. The Fig. 1 below shows the special structure of the residual module.

Fig. 1. Residual module structure.

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3 CLBP Feature 3.1

CLBP Feature Analysis

Complete Local Binary Pattern (CLBP), which is a typically extended feature obtained by deformation on the basis of the Local Binary Patte r(LBP) feature [10]. The original LBP operator takes the central gray value in the window image as a threshold, and other pixels are binary-coded by comparison, thereby converting a decimal number to be the LBP value of all points. CLBP contains CLBP-C, CLBP-M, and CLBP-S, these three description operators improved the quality of feature extraction on this basis [11]. The histograms of the three operators are combined to more effectively extract the local texture features of all original images. 3.2

CLBP Feature Extraction

For the initially collected RGB original image, the weighted average method is first adopted to perform grayscale processing. Then divide the image into 18 * 18 cells, calculate three description operators for each cell, and normalize the histogram distribution. Finally, connect the histograms of each cell. The specific operation is divided into the following two steps [12]: (1) Grayscale: Rn = Gn = Bn = 0.3Ro + 0.59Go + 0.11Bo. (2) Description operator: CLBP-M represents magnitude of the difference between all pixels and the center pixel in the window image. And CLBP-S represents the sign positive and negative.

Fig. 2. Calculation diagram of pixel difference.

First, we use all pixels except the central point in the window image subtract the central value. We gain the result on the right side of Fig. 2. Then we extract the magnitude of positive and negative symbols and absolute values respectively as two eigenvalue operators in Fig. 3.

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Fig. 3. CLBP-S and CLBP-M.

We make these following provisions: dp ¼ sp  mp
sp ¼ signðdp Þ ¼

1 1

ð1Þ dp  0 dp \0

  mp ¼ dp 

ð2Þ ð3Þ

In order to unify the coding of the three descriptors [14], we convert the decimal real number of CLBP-M according to the following formula: CLBP MP;R ¼

P1 X

tðmp ; cÞ2p

ð4Þ

P¼0


tðk; cÞ ¼

1 kc 0 k\c

CLBP CP;R ¼ tðgc ; ci Þ

ð5Þ ð6Þ

In the formula, c is the adaptive threshold which can take the average value of the image, and gc represents the gray value of central pixel point in a window. And ci represents the grayscale average level of all pixels in a window picture.

4 Building a RESNET+CLBP Fusion Network We are using the JAFFE+Cohn-Kanade expression library. First, the image set is normalized by size, and the unified input pixel is 112 * 112. The first layer uses 64 convolution kernels of size 7 * 7, and the pooling layer adopts 3 * 3 max pooling, both of which make a stride of 2. Followed by eight residual modules. Finally, the feature map obtained by the convolution network is further fused with the extracted CLBP texture feature, and then gets into the fully connected layer. After the Softmax function completes the seven classifications, the specific network structure diagram is as follows (Fig. 4):

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Fig. 4. Fusion network structure

5 Experimental Results We conducted the training and test on the computer of the laboratory workstation. The experimental platform was the Jupyter notebook without GPU acceleration. The computer is 8G memory, Intel i5 processor, and the average training time is 10 h. We take the network depth and the network type as variables respectively and make a statistical comparison of the experimental results. (1) The comparison results under different network depths are shown in Table 1. Table 1. Identification rate of different network depths. Number of residual modules Average recognition rate Average training time 4 0.8542 6h 6 0.8956 9h 8 0.9323 12 h 10 0.9723 15 h

By observing the comparison results of the table, we can find that for our network structure and the expression database data, the blind increase of the residual module does not continuously improve the average recognition rate. In the case of 8 residual modules, the average training time is within the acceptable range, and the recognition accuracy is also the highest.

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(2) The comparison results under different network types are performed in Table 2. Table 2. Identification rate of different network types. Network Type CL+RES CL+DCNN CL+DBN CL+SVM

Happiness 95.34 92.15 89.75 87.98

Sadness 93.45 90.56 87.98 89.47

Surprise 96.73 94.32 93.45 90.43

Fear 94.25 92.38 90.56 83.85

Anger 89.75 82.79 83.56 79.24

Disgust 91.56 86.47 85.38 84.72

Neutrality 90.32 84.16 82.17 73.58

Total 93.06 88.98 87.55 84.18

Varied types of network structures have a great impact on the average recognition rate of expressions. It can be seen from the tabular data that the classification accuracy of the support vector machine with simple principle is low, and the performance of the ordinary flat convolution network makes the classification effect greatly jump, however the advantage of the residual network is more significant. For the same network, the sensitivity of different types of expression classification is also uneven. In general, expressions with relatively obvious facial features such as happiness, surprise, and sadness are easier to recognize than micro-amplitude expressions such as neutrality and disgust.

6 Conclusion In view of the difficulty in extracting facial expression features and weak feature description, we choose deep convolutional neural network to obtain image abstract features through network self-learning on the basis of certain generalization ability of the network for expression recognition and classification. The innovations of this paper are mainly reflected in two aspects: one is the selection of the residual neural network, and the shortcut connection module is used to effectively avoid the unfavorable interference from the network depth to the recognition accuracy. Second, feature fusion method is adopted to integrate the extracted local texture features of the sample CLBP with the abstract features output by the convolutional full connection layer, And the fusion features are passed into the classification layer to complete expression recognition. The whole network structure not only reduces the training convergence time, but also improves the average recognition accuracy of facial expressions. Acknowledgment. This work was supported in part by Qingdao Postdoctoral Research Project under Grant 2016021.

References 1. Bashbaghi S, Wei Y, San Z (2018) Gabor feature selection method for expression recognition. Journal 20(1):61–81 2. Hua X, Jun D, Xiu R (2018) Design of driver’s violation call detection system based on CLM. J Qingdao Univ (Eng Technol Edit) 128(02):33, 41–45

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3. Yu X, Gui L, Ye G (2017) Research progress of facial expression in human-computer interaction. In: Conference 2017. IEEE Computer Society 4. Xiao W, Chen X, Min H (2018) expression recognition of video sequences fused with spatio-temporal features. Journal 40(3):626–632 5. Jin S, Ming Y, Jun D (2018) Application of CLNF in face recognition. J Qingdao Univ (Eng Technol Edit) 128(02):33, 51–56 (in Chinese) 6. Pantic M (2016) Facial expression recognition. Journal 2(5):443–447 7. Jin D, Xin W (2018) Adaptive recognition algorithm based on binocular computer vision and its monitoring application. Journal 11(11):1708–1711 8. Meng L, Zheng W, Jiu S (2018) Image classification and recognition based on sparse depth confidence network. Journal 35(09):65–69 9. En L, Xue W, Yu C (2018) Deep deconvolution neural network learning based on deconvolution feature extraction. Journal 33(3):447–454 10. Yan J, Zheng W, Cui Z (2018) Expression recognition based on asymmetric local gradient encoding. Journal 101(4):1217–1220 11. Yuan Y (2018) Facial expression recognition based on depth belief network and multi-layer perceptron. Journal 406(12):155–159 12. Chitta K, Sajjan NN (2017) A reduced region of interest based on approach for facial expression recognition from static images. In: Conference 2017. IEEE

Research and Application on Ensemble Learning Methods Yuzhong Wang(&) Zhonghuan Information College Tianjin University of Technology, Tianjin, China [email protected]

Abstract. As shown in previous data, diabetes has led to the increasing mortality and considerable financial expenditure in the US. It is necessary to find out how to making correct diagnosis and prescription of diabetes plays an important role in helping patients. That is why we choose the dataset of diabetic inpatients having diagnosis at hospitals in the US, and predict how different treatments and medications influence patient outcomes. We use the class attribute of readmission number to obtain the results. Because of the large and biased dataset, we firstly remove attributes with high missing value rate, and reduce the imbalance classes of instances by oversampling and under-sampling, then followed by the attribute selection through various methods, such as the Correlation-based feature selection, the ChiSquared Attribute Evaluator, the Information Gain Attribute Evaluator, etc. Three classification methods C4.5, RIPPER, and Random Forests are used to predict the classification in Weka. In addition, we also use the ensemble learning methods including bagging and boosting to improve the stability and accuracy. From the analysing results, we can see that C4.5 and Ripper perform better, and both bagging and boosting increase the accuracy rate to differing degrees because both algorithms are somewhat unstable. There is no doubt that Random Forests is the best performer among all classification methods we use, and after using boosting, we see big increases in the values of the evaluation metrics we use. The final outcome is much better than random guess. Keywords: Dataset
Data Preprocessing
Ensemble learning
Data mining
Classification Models

1 Introduction Medicine and healthcare are among the main areas of research in data mining, while diabetes is one of the leading causes of death in the US, affecting approximately 9.3% of the US population. The total costs of diabetes in the US were estimated at an astounding $245 billion in 2012, up from $174 billion in 2007 [1]. The dataset that we have chosen to work with is based on the treatment of 101,252 inpatients at hospitals across the US who have been diagnosed of diabetes. The data was collected over a period of 10 years (1999–2008). There have previously been very few national assessments of diabetes care during hospitalization in the US and it is believed that a change in care could positively impact patient outcome. © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 145–155, 2020. https://doi.org/10.1007/978-981-32-9050-1_17

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The significance of this issue, and the positive impact that an improvement in the US diabetes crisis would have are our main reasons for choosing the dataset analysed in this report. 1.1

About the Dataset

The data were submitted to the UCL machine learning repository on behalf of the Center for Clinical and Translational Research, Virginia Commonwealth University in 2014. The instances in this dataset were initially taken from a much larger original dataset with 74,036,643 instances and 55 attributes according to five criteria (1. The patient was admitted to hospital; 2. The diagnosis of the patient was of diabetes; 3. The length of stay was at least 1 day and at most 14 days; 4. Laboratory tests were performed during the admission; 5. Medications were administered during the admission). There are also very few missing values relative to the size of the dataset. This and the size of both the dataset and its attributes were important considerations in our choice of dataset. The attribute of interest (the class) in our dataset is the readmission rate, including three categories (1. Readmission in under 30 days from initial discharge; 2. Readmission in over 30 days; 3. No readmission). Among the 101,252 individuals included, 11,357 were readmitted in under 30 days, 35,545 were readmitted in over 30 days and the remaining 54,867 were not readmitted. The other 49 attributes cover patient demographics, details of the treatment during admission and also that of any treatment in the year prior to admission. Below is a list of the attributes included in the resulting dataset (Table 1): Table 1. Attributes Attribute Encounter ID Patient number Race Gender Age Weight Admission type

Attribute type Numeric Numeric Nominal Nominal Nominal Numeric Nominal

Discharge Nominal disposition Admission source Nominal

Time in hospital

Numeric

Description Unique identifier of an encounter Unique identifier of a patient Caucasian, Asian, African, American, Hispanic, other Values among: male, female, and unknown/invalid Grouped in 10-year intervals: [0, 10), [10, 20),…, [90,100) Weight in pounds Integer identifier corresponding to 9 distinct values, for example, emergency, urgent, elective, newborn, and not available Integer identifier corresponding to 29 distinct values, for example, discharged to home, expired, and not available Integer identifier corresponding to 21 distinct values, for example, physician referral, emergency room, and transfer from a hospital Integer number of days between admission and discharge (continued)

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Table 1. (continued) Attribute Number of lab procedures Number of procedures Number of medications Number of outpatient visits Number of emergency visits Number of inpatient visits Diagnosis 1 Diagnosis 2 Diagnosis 3 Number of diagnoses Glucose serum test result A1c test result

Attribute type Numeric

Description

Numeric

Number of procedures (other than lab tests) performed during the encounter Number of distinct medications administered during the encounter Number of outpatient visits of the patient in the year preceding the encounter Number of emergency visits of the patient in the year preceding the encounter Number of inpatient visits of the patient in the year preceding the encounter The primary diagnosis Secondary diagnosis Additional secondary diagnosis Number of diagnoses entered to the system

Numeric Numeric Numeric Numeric Nominal Nominal Nominal Numeric Nominal Nominal

Change of medications Diabetes medications 24 features for medications

Nominal

Readmitted attribute

Nominal

1.2

Nominal Nominal

Number of lab tests performed during the encounter

Indicates the range of the result or if the test was not taken. Values: >200, >300, Normal, and None if not measured Indicates the range of the result. Values: >8, >7 and > 2p > i ¼ I sin wt þ > B m > 5 >
 > < 4p iC ¼ Im sin wt þ 5
 > 6p > > iD ¼ Im sin wt þ 5 > > >
 > > 8p : iE ¼ Im sin wt þ 5 Among them, Im is the current amplitude. When the magnetic momentum of each phase is defined as Then the magnetic momentum component of the 5-phase output is defined as 8 FA ¼ 12 N
Im sinxt cos b   >  > > 2p > < FB ¼ 12 N
Im sinxt þ 2p 5  cosb þ 5  4p FC ¼ 12 N
Im sin xt þ 4p 5  cos b þ 5  > 1 6p 6p > > > FD ¼ 2 N
Im sin xt þ 5  cos b þ 5  : 1 8p 8p FE ¼ 2 N
Im sin xt þ 5 cos b þ 5

ð1Þ

.

ð2Þ

In the formula, is the number of turns per phase winding and b is the electrical angle along the space position around the air gap with phase A axis as the origin. Expansion of formula (2) shows that the magnetomotive force of 5-phase PM motor is F ¼ FA þ FB þ FC þ FD þ FE 5 ¼ N
Im ðsin xt cos b  cos xt sin bÞ 4 5 ¼ N
Im sinðxt  bÞ 4

ð3Þ

3 Fault-Tolerant Control Strategy The common FTC methods are vector control (VC) and direct torque control (DTC). The core of VC strategy is to control the stator current vector. The specific principle is to decompose the stator current vector of the motor into two components as excitation current and torque current. According to the principle of magnetic field orientation (MFO), the amplitude and phase of the two components are controlled respectively, and the goal of motor control is finally completed. By decoupling the flux linkage and the torque, the regulators of the two are designed respectively to achieve high-performance speed regulation of the motor.

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There are several commonly used vector control methods: id = 0 control, cos u ¼ 1 control, maximum thrust current ratio vector control, etc. Direct Torque Control (DTC) is a control method centered on the control of torque. It does not simply control the torque by controlling the intermediate variables such as current and flux linkage, but directly takes the torque as the control variable. Although stator field orientation is also used, DTC does not need complex coordinate transformation compared with vector control. DTC uses space vector to directly control the torque in the stator coordinate system. The principle of stator MFO is adopted with the PWM technology. The switching state of the inverter is controlled directly, and the optimal control effect is obtained. Compared with vector control, the establishment of complex mathematical model is omitted. Compared with direct torque control, vector control has a wider speed range by rotating coordinate transformation. For the smoothness of torque control, there is no torque ripple. Compared with the background requirements of electric vehicles and manufacturing, vector control method can better meet the requirements of this paper. When phase A is open, the phase current is 0. If the remaining four phases remain unchanged in amplitude and phase, the circular magnetic field generated by the rotor will become elliptical, the motor may oscillate and the electromagnetic torque will pulsate. According to the principle of invariant magnetomotive force MMF, the circular magnetic field can be generated by changing the amplitude and current phase in the remaining four phases. How to obtain the magnitude and phase of the remaining fourphase current has been deduced by the principle that magnetic potential remains unchanged and given two constraints. In this paper, two constraints of the motor are given: (1) the current amplitude in the remaining four phases is the same after single-phase open-circuit fault occurs; (2) the phase symmetry of the current in other four phases after the assumed fault occurs. The results are given directly without detailed derivation. Assuming that phase A is open, the instantaneous expression of residual fourphasecurrent after fault is as follows 8 iA ¼ 0 > > > > < iB ¼ 1:382Im sinðxt þ 36 Þ iC ¼ 1:382Im sinðxt þ 144 Þ > > > iD ¼ 1:382Im sinðxt  144 Þ > : iE ¼ 1:382Im sinðxt  36 Þ

ð4Þ

On the basis of the expression of fault-tolerant current after fault occurs, a model of permanent magnet motor driven by voltage source is established, which is the FTC method in this paper: (1) The two orthogonal bases of the fundamental wave subspace are obtained from the phase current in Eq. (4), and the generalized Park matrix of the transformation from four-phase to two-phase stationary coordinate system is obtained. (2) By multiplying the extended Park matrix with Clark matrix, the coordinate transformation matrix is obtained, and the current transformation from natural coordinate system to synchronous rotating coordinate system is realized.

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(3) Through the product of Clark matrix and inverse transformation matrix of generalized Park matrix, the voltage in synchronous rotating coordinate system is transformed to the voltage in natural coordinate system, and the control voltage is obtained by adding the opposite electromotive force. The control of motor is realized by CPWM method. Among them " T4s=2s ¼

cos 0:2p 3:618 sin 0:2p 1:91

cos 0:8p 3:618 sin 0:8p 1:91

2

T2s=4s

cos 0:2p 6 cos 0:8p ¼6 4 cos 1:2p cos 1:8p

cos 1:2p 3:618 sin 1:2p 1:91

cos 1:8p 3:618 sin 1:8p 1:91

#

3 sin 0:2p sin 0:8p 7 7 sin 1:2p 5 sin 1:8p

ð5Þ

ð6Þ

For a 5-phase motor, when a fault occurs in a certain phase, in order to make the origin of the spatial electric angle of the motor (i.e. the axis of phase A) coincide with the axis of the fault phase of the motor and have the same direction, the corresponding angle of the natural coordinate system should be rotated counterclockwise. The general Clark transformation matrix is as follows:
 cos xt  2k5 p
 ¼4  sin xt  2k5 p 2

C2s=2r


3 sin xt  2k5 p
5 cos xt  2k5 p

ð7Þ

Among them, k = 0, 1, 2, 3, 4. In this paper, if phase A is open, then  C2s=2r ¼

cos xt  sin xt

sin xt cos xt



  cos xt  sin xt C2r=2s ¼ sin xt cos xt

ð8Þ ð9Þ

The steps (2) can be represented by the following coordinate transformation matrix 

id iq



2

3 iB 6 iC 7 7 ¼6 4 iD 5T4s=2s C2s=2r iE

In the synchronous rotating coordinate system, the corresponding voltage in the rotating coordinate system are obtained.

ð10Þ

and

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Among them, step (3) can be expressed by the following formula: 2

3 uB   6 uC 7 6 7 ¼ ud C2r=2s T2s=4s 4 uD 5 uq uE

ð11Þ

The following figure is a block diagram of FTC method for 5-phase PM motor when one-phase open-circuit fault occurs (Fig. 1).

C T

inverter

PMSM

Fig. 1. Block diagram of fault-tolerant control method for 5-phase PM motor

4 Fault-Tolerant Control Strategy 4.1

Simulation Model and Results

In view of the above strategy, the mathematical model of 5-phase PM motor and the following FTC system are established in Simulink with the help of MATLAB software (Fig. 2). The simulation system consists of coordinate transformation module, PI regulator, motor model and CPWM module. In the simulation process, a phase fault signal is given at 0.05 s after the start of operation. This paper gives that phase A has an open circuit fault. From the analysis of the simulation waveforms, it’s concluded as follow: (1) After 0.05 s, the A phase current is 0, and the remaining four normal phases are increased to the same extent in a short time. The magnitude is approximately the same, which meets the constraints.

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Fig. 2. Simulation system model

(2) After starting the motor, when it reaches a stable state, the electromagnetic thrust value is stable at about 800 N. After a short period of rapid adjustment, the electromagnetic thrust of the motor is again maintained at about 800 N, which is the same level as before the fault. During the adjustment process, the motor speed has not changed before and after the fault. (3) Current values id and iq are monitored in two-phase rotating coordinate system, and waveform pulsation exists only in the instant of fault, which can quickly return to steady state, indicating that current pulsation is effectively suppressed, or that the torque pulsation is also effectively and rapidly suppressed. 4.2

Simulation Model and Results

Figure 3 displays the current waveforms of each phase before and after open circuit fault under fault-tolerant control strategy. Figure 4 displays the electromagnetic thrust waveform of the motor under faulttolerant control strategy before and after phase A open-circuit fault. Figure 5 displays the speed waveform of the motor before and after the open-circuit fault. Figure 6 displays the id and iq current waveforms of the motor before and after the A phase open circuit fault. Figure 7 displays the waveform of the C phase current before and after the A phase open circuit fault under fault-tolerant control strategy.

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Fig. 3. Current waveforms of motor phases

Fig. 4. Electromagnetic thrust waveform

Fig. 5. Speed waveforms

Fig. 6. Id and iq current waveforms

Fig. 7. C phase current waveforms

5 Conclusion Aiming at one-phase open-circuit fault of 5-phase PM motor, a fault-tolerant control method based on fault-tolerant current calculation and CPWM technology is advanced. The coordinate transformation of voltage and current is realized by deducing Park matrix and Clark matrix and corresponding inverse transformation matrix, and the expected phase voltages of the motor in natural coordinate system are obtained. Fivephase permanent magnet motor is controlled by voltage source-driven inverter and CPWM modulation technology. Fault-tolerant control of motor in specific fault is realized. According to the theoretical analysis, the system simulation model is built. The simulation and waveforms show that the FTC strategy proposed in paper is

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feasible. When one-phase fault occurs, it can maintain the motor’s torque output, lower the motor’s torque pulsation, and realize the state switching before and after the motor fault.

References 1. Bianchi N, Bolognani S, Pre MD (2007) Strategies for the fault-tolerant current control of a five-phase permanent-magnet motor. IEEE Trans Ind Appl 43(4):960–970 2. Baudart F, Dehez B, Matagne E et al (2013) Torque control strategy of polyphase permanentmagnet synchronous machines with minimal controller reconfiguration under open-circuit fault of one phase. IEEE Trans Ind Eletron 59:2632–2644 3. Mohammadpour A, Parsa L (2013) A unified fault-tolerant current control approach for fivephase PM motors with trapezoidal back EMF under different stator winding connections. IEEE Trans Power Electron 28(7):3517–3527 4. Gao Y, Zhao W, Liu G et al (2013) Research on fault tolerant control of new five-phase fluxswitched permanent magnet motor. Micromotors 46(1):33–36 (in Chinese) 5. Zhou H, Liu G, et al (2015) A Fault-Tolerant Control Method for Five-Phase Fault-Tolerant Permanent Magnet Linear Motor. CN104682820A (in Chinese) 6. Zheng P, Tang P, Sui Y et al (2013) Fault tolerant control strategy of five-phase permanent magnet synchronous motor for electric vehicle. Electr Mach Control 17(10):65–69 (in Chinese) 7. Gao H (2016) Research on Driving and Fault Tolerant Control Technology of Five-Phase Permanent Magnet Synchronous Motor (in Chinese)

Enhanced Pulse Density Modulation for Efficiency Optimization in Inductive Power Transfer Systems Hong Zheng(&), Rui Bian(&)

, and Yubing Gu(&)

School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China {zhenghong0511,gyb1778300805}@sina.com, [email protected]

Abstract. An enhanced pulse density modulation (EPDM) is proposed to optimize the efficiency in inductive power transfer (IPT) systems under various coupling and load. It controls the optimal output voltage without estimating the mutual inductance, which improves the efficiency and eliminates the communication between the primary and secondary side. Further, the four working modes of semi-bridgeless active rectifier (S-BAR) are combined uniformly by the EPDM based on DR modulation, which increases density resolution and decreases the output voltage ripple. Besides, the soft switching can be ensured by synchronous signals. The effectiveness of the method is confirmed by simulations and experiments. Keywords: Inductive power transfer (IPT)
Efficiency optimization Voltage regulation
Enhanced pulse density modulation (EPDM)
DR modulation




1 Introduction Inductive power transfer (IPT) transmits energy by magnetic coupling instead of wires [1]. With superior performances in safety and convenience [2], IPT has been preferred in changers of electric vehicles, mobile phones and biomedical implants [3]. IPT systems are always desired to operate with the high efficiency. However, the system efficiency is affected by changes of coupling coefficient and load resistance. The systems with optimal topology and parameters can improve the efficiency [4], but operating condition variations still affect the efficiency. A better method, maximum efficiency point tracking (MEPT), was been presented to address the problem. Generally, MEPT is performed on the secondary side by using a dc/dc converter which allows the value of the equivalent load resistance to be optimal. And the output voltage is stabilized by the control on the primary side [5]. However, added converters increase loss, complexity and cost. To avoid the additional loss, the inverter and active rectifier can be controlled by phase shift modulation [6], but it may pose the hard switching. And above methods need communication between the dual sides which will cause slow dynamic response. Maximum efficiency control without communication © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 166–173, 2020. https://doi.org/10.1007/978-981-32-9050-1_19

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link was presented in [7]. MEPT and voltage regulation were realized by means of pulse density modulation (PDM) on the dual sides, which ensured soft switching of the inverter and rectifier. Although open-loop control can avoid communication link, its dynamic response was still not ideal. In this paper, an EPDM for efficiency optimization is proposed. It is only applied on the secondary side without communication link. With this method, maximum efficiency, stable output voltage and soft switching can be achieved simultaneously.

2 Conditions for Maximum Efficiency Figure 1 is the equivalent circuit of the series-series (SS) compensated IPT system, where Li, Ci, Ri (i = 1, 2) are the self-inductances, compensated capacitors and equivalent series resistors respectively. M and RE represent the mutual inductance and the equivalent load resistance. U1 and U2 represent the input and output voltages. i2

i1

C1

C2

M U1

L1

L2

RE

U2

R2

R1

Fig. 1. Topology of IPT system with SS compensation

In [8], the system efficiency η and U2 are expressed as: g¼

x2 M 2 RE ðR2 þ RE Þðx2 M 2 þ R1 R2 þ R1 RE Þ

ð1Þ

xM RE U1 R1 R2 þ R1 RE þ x2 M 2

ð2Þ

U2 ¼

where x represents system operating angular frequency. From (1) and (2), η and U2 both depend on M and RE in dynamic system. To maximize η, the derivative of η relative to RE should be zero, and the optimal RE can be expressed as: RE;opt

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi rffiffiffiffiffi x2 M 2 R2  xM ¼ R2 1 þ R1 R2 R1

ð3Þ

for (xM)2  R1R2. By substituting (3) into (2), the optimal U2 can be obtained: U2;opt

rffiffiffiffiffi R2  U1 R1

ð4Þ

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for (xM)2  R1(R2 + RE). In a specific IPT system with fixed R1, R2 and input voltage, the output voltage can be stabilized at a fixed value to ensure the high efficiency.

3 EPDM for Efficiency Optimization 3.1

Principle of EPDM

Figure 2 is the circuit of a SS-IPT system with S-BAR. PDM can control the load voltage by removing some pulses of u2. And the proportion of the remaining pulses to the whole switching period is named pulse density, being expressed as d. Figure 3 shows the EPDM pattern compared with the SPDM under different d.

S1

RE

S3 u1

Vin S2

S4

D1

C1 i1 C M i2 2 L1

u2

L2

R1

R2 S5

Cf D2 S6

I0

D3

RL V0

D4

Fig. 2. Circuit diagram of IPT system with S-BAR

u2

SPDM t t

u2 d=0.7

EPDM

d=0.3

t t

Fig. 3. Waveforms of the S-BAR’s input voltage with SPDM and EPDM

SPDM has two kinds of waveforms, square waves and ‘0’, increasing and decreasing voltage, respectively. Therefore, u2 should be distributed uniformly to minimize ripple. Note that the output power is zero during ‘0’. In order to further shorten the duration of the ‘0’, EPDM with additional positive square waves is proposed. For EPDM, u2 has no “0” at density of 0.5-1 and owns square waves and ‘0’ at density of 0–0.5. The S-BAR has various operating modes with different switching conditions of S5 and S6, the possible operating waveforms of S-BAR is shown in Fig. 4. Mode 0: The switches S5 and S6 are both turned off. The positive (negative) current i2 charges Cf and transmits energy to the load through D1 (D3) and D4 (D2). Mode 1: The switch S5 (S6) is turned off and S6 (S5) is turned on. The positive (negative) current i2 flows to D1 (D3) and S6 (S5). During this interval, i2 charges the filter capacitor Cf and the power flows into the load. Mode 2: The switch S5 is turned off and S6 is turned on. In the former half of the cycle, the positive current i2 delivers power to the load through D1 and S6. And in the

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latter half of the cycle, the secondary side circuit is shorted and the negative current i2 flows through S6 and D2. The energy is exchanged between L2 and C2. Mode 3: The switch S5 is turned off (on) and the switch S6 is turned on. In the former half of the cycle, the positive current i2 flows through D1 and S6, delivering power to the load. And in the latter half of the cycle, the secondary side circuit is shorted and the negative current i2 flows through S6 and S5. u2 ,i2 t ugs_S5 t ugs_S6 0

1

2

3

4

t

Fig. 4. Possible operating waveforms of S-BAR

Mode 4: The switches S5 and S6 are both turned on. The positive (negative) current i2 flows to S5 (S6) and S6 (S5). The secondary side circuit is shorted and the load is supplied only by filter capacitor Cf. In mode 0 and mode 1, u2 is a square wave, and there is a positive square wave in mode 2 and mode 3, and mode 4 is ‘0’. Suitable modes should be selected to combine the three waveforms, where the mode 0 and mode 1 are selected to achieve a combination of square waves, and mode 2, 3 combine the square waves and the ‘0’. 3.2

Voltage Regulation

Form (2), the following equation can be obtained: U2 ¼

xM RE R1 ðR2 þ RE Þ þ ðxM Þ

2

U1 

RE U1 xM

ð5Þ

for (xM)2  R1(R2 + RE). The waveform of u2 is uniform with magnitude of U2m and pulse density of d under PDM. It can be balanced with a waveform with magnitude of U2m * d and pulse density of 1. The average output power can be expressed as: pffiffiffi 2 2 d V0 I2 P0 ¼ U2 I2 ¼ p

ð6Þ

where d is the pulse density of u2. Combining (6) with the power balance principle, the equivalent load resistance RE can be derived: RE ¼

8 2 d RL p2

ð7Þ

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By substituting (7) into (5) and considering the characteristics of power electronic devices, the load voltage can be obtained: V0 ¼

8 RL dV in p2 xM

ð8Þ

From (8), it is possible to stabilize the load voltage by adjusting pulse density.

4 Control Operation An EPDM for S-BAR is designed to obtain the optimal output voltage. And the control schematic of S-BAR is shown in Fig. 5. A double PI control is used to figure out the pulse density d in real time, which is needed for voltage stabilization. By comparing the relationship between d and 0.5, suitable operating modes can be chosen. Then d1 calculated by the algorithm corresponding to the modes is sent to EPDM, whose input pulses are synchronous with i2. The EPDM module generates gate signals corresponding to d to act on S-BAR.

i2 Vref

PI

Synchronous signal generator PI

d

Mode EPDM selection d1

ugs_S5 ugs_S6

V0

Fig. 5. Control block diagram of S-BAR

As mentioned above, PDM pulses are needed to be uniform with high density resolution. Figure 6 shows the block diagram of PDM. This controller consists of input pulses synchronized with i2, a DR modulator and an AND gate. In the DR modulator, the difference between the values of the input d1 and the output signal is sent to the integrator, which is triggered by the rising edge of the input pulses. A feedforward is introduced to solve the delay generated by the integrator. And then the comparator is designed as ‘  0.6’ to realize quantization, so that the output signal is the uniform combination of ‘1’ and ‘0’. Modulated pulses proportional to d are achieved by the AND gate, whose input signals are the synchronous signal and the output of the DR modulator. In order to realize the pairwise combinations of the operating modes, it is necessary to select two modes according to the d value, considering two situations: (1) 0.5  d  1; (2) 0 < d < 0.5. As shown in Fig. 7, in the first case, two complementary pulses generated by the PDM module trigger S5 and S6 to realize the uniform combination of mode 1 and mode 2. At that situation, d = 0.5 + 0.5d1. In the second case, S6 continues to be turned on and S5 is driven by the pulses that is complementary to the modulated pulses of PDM to combine mode 3 and mode 4, there is d = 0.5d1. EPDM is finished in this way.

Enhanced Pulse Density Modulation for Efficiency Optimization

d1

d1

PDM

ugs_S5 NOT ugs_S6

0.6 modulator

d1 PDM NOT

ugs_S5

1

ugs_S6

(a) 0.5≤d≤1

Fig. 6. Block diagram of PDM

171

(b) 0 < z_ 1 ¼ z2  l1 e ð6Þ z_ ¼ z3  l2 e þ b0 u > > : 2 z_ 3 ¼ l3 e Where, Z ¼ ðz1 ; z2 ; z3 ÞT is a state observation vector. l1 , l2 and l3 are observer gains. LESO characteristic equation is OðsÞ ¼ s3 þ l1 s2 þ l2 s þ l3

ð7Þ

In order to accurately and real-time estimate states and total disturbance of the controlled object, how to selected parameters l1 , l2 and l3 , that is, observer gains, is very important. The observer gains can be designed by pole assignment method. Generally, the form of the characteristic equation which is better stable and transition process is OðsÞ ¼ ðs þ xo Þ3

ð8Þ

That is, three poles of the observer are assigned the same position xo . We define the observer bandwidth as xo . So, we obtain l1 ¼ 3xo ; l2 ¼ 3x2o ; l3 ¼ x3o

ð9Þ

Study of Speed Stabilization Loop for Airborne Photoelectric Platform

185

The observed states z1 ; z2 ; z3 are the estimates value of the system states and total disturbance x1 ; x2 ; x3 ð f Þ, respectively. Based on the observed states z1 ; z2 ; z3 , the controller can be designed as u ¼ ðu0  z3 Þ=b0

ð10Þ

Ignoring the observation error, the original system model is simplified to €y  u0

ð11Þ

by taking Eqs. (10) into (1). Using a simple PD controller, for example u0 ¼ k p ð r  z 1 Þ  k d z 2

ð12Þ

Then, output y of the controlled object can be controlled. Where r is input value of given reference. kp and kd are gains of PD controller. So, the characteristic equation of PD controller is GðsÞ ¼ s2 þ kd s þ kp

ð13Þ

The pole assignment method can also be used to design PD controller gains. We take GðsÞ ¼ ðs þ xc Þ2

ð14Þ

kp ¼ x2c ; kd ¼ 2xc

ð15Þ

So, we obtain

We define PD controller bandwidth as xc and observer bandwidth as xo . In general application, we can select the relationship between xc and xo as follows xo ¼ ð3v5Þxc

ð16Þ

LADRC only tunes two parameter b0 and xc . It is simple in controller structure and easy to carry in engineering [5, 6].

3 Application of ADRC to Speed Stabilization Loop for Airborne Photoelectric Platform 3.1

Mathematical Modeling

When the viscous friction and friction moment are neglected in the modeling of the controlled object of the photoelectric platform, the transfer function of the speed stabilization loop is as follows

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G x ðsÞ ¼

h_ ðsÞ 1=kb ¼ uðsÞ ðTm s þ 1ÞðTe s þ 1Þ

ð17Þ

Where kb is the motor’s back EMF coefficient, Tm is the motor’s electromechanical time constant and Te is the motor’s electromagnetic time constant. In this paper, take the speed stabilization loop control system of the elevation frame for a photoelectric platform as an example, the parameters are measured. Transfer function is as follows Gx ðsÞ ¼

h_ ðsÞ 1:45 1435:5 ¼ ¼ s þ 1 s þ 1 uðsÞ ð1=9 Þð1=110 Þ ðs þ 9Þðs þ 110Þ

ð18Þ

External disturbance torque is applied at the output of the motor torque so as to simulate anti-jamming performance. In the case, the disturbance transfer function is as follows G d ðsÞ ¼

h_ ðsÞ La s þ Ra 0:628 5:652 ¼ G x ðsÞ
¼ ¼ d ðsÞ 1=9s þ 1 sþ9 km

ð19Þ

Where km is moment coefficient, La is armature inductance and Ra is armature resistance. When both the speed reference input and the disturbance moment input are simultaneously applied to the system, the speed output of the controlled object can be obtained with Eqs. (18) and (19) as follows h_ ðsÞ ¼

3.2

1435:5 5:652 uðsÞ þ d ðsÞ ðs þ 9Þðs þ 110Þ sþ9

ð20Þ

Traditional Controller Design

The traditional controller design method of the speed stabilization loop of photoelectric platform is based on frequency domain theory. According to the system performance requirements, a lag-ahead series correction method is adopted to improve the system performance and system bandwidth. Generally, the closed-loop bandwidth of the speed stabilization loop is not less than 25 Hz. According to the mathematical model Eq. (18), the transfer function of the designed lagged-ahead series network is as follows Gc ðsÞ ¼ 3404


3.3

1=24s þ 1 1=105s þ 1
1=0:065s þ 1 1=250s þ 1

ð21Þ

LADRC Design

For a closed-loop second-order system with damping ratio n ¼ 1, relationship between its closed-loop bandwidth frequency xb and undamped natural frequency xn (i.e. PD controller bandwidth as xc ) is as follows

Study of Speed Stabilization Loop for Airborne Photoelectric Platform

xb ¼ 0:644
xc

187

ð22Þ

According to the performance requirement xb  25 Hz ¼ 157 rad/s, xc  244 rad/s can be obtained. The bandwidth frequency of PD controller can be selected as xc ¼ 250 rad/s. By using Eq. (16), the bandwidth frequency of the observer can be selected as xo ¼ ð3v5Þxc ¼ 900 rad=s: The above LADRC design unifies the modeled part, the unmodeled part and the external disturbance into the generalized disturbance of the system. The alternative ADRC can be constructed by using the modeled part, which can reduce the burden of the observers. The parameters of observer and PD controller in the alternative LADRC are the same as those in LADRC. 3.4

Simulation Results

In MATLAB/Simulink, the speed stabilization loop control system of the elevation frame for a photoelectric platform is simulated. The traditional control, LADRC and the alternative LADRC are shown in Figs. 1, 2 and 3.

Disturbance Output

21.95 Input signal

Controlled object

Controller

Fig. 1. Block diagram of closed-loop with traditional control

Disturbance 250^2 v0

Input signal

1/1435.5

2*250

u

Controlled object

PD controller 1435.5

900^3

3*900^2

-1

1 s

3*900

z3

1 s

Linear extended state observer (LESO)

Fig. 2. Block diagram with LADRC

z2

1 s

z1

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Step response of three controllers is shown in Fig. 4. So as to test anti-jamming performance for three controllers, a step disturbance is added to the system at t ¼ 0:2 s. The system response is shown in Fig. 5. We can find from Figs. 4 and 5, the alternative LADRC has the fastest response speed, no overshoot and the strongest disturbance suppression ability. Assuming 1=kb , Tm and Te increased by 50% respectively, the system response is shown in Fig. 6. We also find that alternative LADRC has good performance of overcoming the system parameter variation. Figure 7 shows the response comparison under the condition of system parameter variation with alternative LADRC. Similarly, it is shown that alternative LADRC has a good ability to overcome parameter perturbation under various parameter changes. In summary, it can be seen that the alternative LADRC is an ideal controller.

Disturbance

250^2 v0

Input signal

1/1435.5

2*250 PD controller

u

Controlled object

990 1435.5 119

z1 z2

900^3

3*900

3*900^2

-1

1 s

z3

Linear extended state observer (LESO)

1 s

1 s

z2

z1

119 990

Fig. 3. Block diagram with alternative LADRC

Fig. 4. Step response plots

Fig. 5. Anti-disturbance response plots

Study of Speed Stabilization Loop for Airborne Photoelectric Platform

Fig. 6. Parameter variation response plots

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Fig. 7. Parameter perturbation 50% response plots

4 Conclusions In the paper, application of LADRC in speed stabilization loop control of photoelectric platform is studied. First, the design method of second-order control system LADRC is analyzed, and the advantages of its simple structure and easy engineering implementation are pointed out. The corresponding controllers are designed by using traditional design method, LADRC, alternative LADRC. The step response and anti-disturbance simulation of the design results are carried out. The simulation results show that alternative LADRC has the fastest response speed, no overshoot and remarkable disturbance suppression effect. It is a universal control structure independent of model and can obtain better response and anti-disturbance performance than PID control. The alternative LADRC is planned to be implemented by hardware, and its control effect in the speed stabilization loop of the photoelectric platform is tested in the experiment. Acknowledgement. The work is partially supported by Heilongjiang University Special Foundation by Basic Scientific Research in Universities of Heilongjiang Province (No. KJCX201824).

References 1. Chen Z, Cheng Y, Sun M (2017) Surveys on the theory and engineering applications for LADRC. Inf Control 46(3):257–266 (in Chinese) 2. Han J (2013) Active disturbance rejection control technique. National Defense Industry Press, Beijing (in Chinese) 3. Gao Z (2013) Research on ADRC thought. Control Theory Appl 30(12):1498–1510 (in Chinese) 4. Gao Z (2003) Scaling and bandwidth-parameterization based on control tuning. In: Proceedings of the American control conference, pp 4989–4999 (2003)

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5. Herbst G (2016) Practical active disturbance rejection control: bumpless transfer, rate limitation and incremental algorithm. IEEE Trans Ind Electron 63(3):1754–1762 6. Godbole A, Kolhe J (2013) Performance analysis of generalized extended state observer in tackling sinusoidal disturbances. IEEE Trans Control Syst Technol 21(6):2212–2223 7. Wan H (2001) Stability and application for active-disturbance-rejection-controller. Academy of Mathematics and Systems Science, Beijing. (in Chinese)

Active Disturbance Rejection Control of Drum Water Level with Generalized Extended State Observer Cuiping Pu1, Jie Ren1, and Jianbo Su2(&) 2

1 Kunming University, Kunming 650214, China Shanghai Jiao Tong University, Shanghai 200240, China [email protected]

Abstract. Time-varying disturbance of drum water level is difficult to estimate and compensate, which leads to time-varying periodic error of the whole system. System model is reconstructed by using prior information of equivalent disturbance, and the components of known modes in equivalent disturbance are estimated and compensated by the generalized extended state observer. Thus, the error can be eliminated and the control accuracy of the system can be improved. Compared with the traditional active disturbance rejection control (ADRC) system based on linear extended state observer, the control performance under different types of external disturbances is studied. Simulation results show that the proposed generalized extended state observer can completely estimate and compensate the components of the known modes in the equivalent disturbance with small overshoot, short adjustment time, strong antiinterference ability and high control precision. Keywords: ADRC


State observer
Drum water level

1 Introduction Drum water level is an important parameter of boiler operation. At present, serial threeimpulse control [1] is usually used in industry to regulate drum water level, especially serial PID control. Because of the changeable operating conditions and the complexity of combustion system, the drum water level is often severely disturbed. Therefore, the conventional PID control has poor control effect on the drum water level [2]. Aiming at drum water level control, Liu [3] and others introduce extended Kalman neural network method to design network controller (EK-NNC) to replace conventional PID controller. Li [4] etc., propose a model reference adaptive internal model control (IMC) method based on fuzzy reasoning. Rong [5] etc., use the PID controller of the fuzzy neural network as the water level regulator of the boiler drum in the feed water control system, which can adjust the parameters of the PID controller in real time. Active Disturb Rejection Control (ADRC) [6–11] was proposed by Han in the 1980 s. The main idea is to treat the uncertainty of the model and the external disturbance of the system as the total disturbance. As the core part of ADRC, Extended State Observer (ESO) [12] models and reconstructs the total disturbance of the system [13] as the extended state of the system, so that the state observer can estimate the © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 191–199, 2020. https://doi.org/10.1007/978-981-32-9050-1_22

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equivalent disturbance of the system at the same time. With the development of ADRC theory and technology, it has gradually been familiar with control society and industry and widely used in various practical control problems, such as robot [14, 15] and manipulator [16], aircraft control, power system control, etc. In drum water level control, Fu [17] etc., estimated the control gain with the formula of feed water flow, estimated the system state with linear expansion observer, and took measures against actuator saturation. Jiang [18] etc., proposed that the objective function should be composed of the variation of error and control variables, and the weighting factor of the objective function should be selected so as to turn the tuning problem of controller parameters into an optimization problem. Compared with traditional PID cascade control, the system has better anti-jamming ability and robustness. But they mainly discuss the gradual tracking of constant external disturbances. However, the time-varying disturbances in practical applications cannot be fully estimated by the traditional ESO. Therefore, it is very important to study the design strategy of observers for time-varying disturbances. In this paper, a generalized linear extended state observer (GLESO) based on the reconstructed model is designed to fully estimate and compensate the known model components in the equivalent disturbance and improve the control accuracy of the system. The anti-jamming performance of traditional linear extended state observer (LESO) and GLESO for drum water level under different external time-varying disturbance is simulated and analyzed. The results show that the ADRC system based on GLESO has the advantages of small overshoot, short adjustment time, strong antijamming ability and high control precision, and has good engineering application value and application prospect.

2 Linear Active Disturbance Rejection Control for Drum Water Level ADRC [19–21] is mainly composed of tracking differentiator (TD), extended state observer (ESO) and non-linear state error feedback (NLSEF) [22]. The structure of ADRC is shown in Fig. 1. ADRC arranges the system transition process by TD to reduce the system oscillation and overshoot. ESO is used to estimate the internal state and equivalent disturbance of the system. Finally, NLSEF is used to stabilize the nominal system compensated by the observer. If ESO and NLSEF adopt linear functions, ADRC can be simplified to LADRC [23–25] which is more conducive to parameter tuning [26] and theoretical analysis.

Fig. 1. Structural of ADRC

Fig. 2. Transfer function model of drum water level system.

Active Disturbance Rejection Control of Drum Water Level

2.1

193

Linear Extended State Observer (LESO)

In LADRC, the linear extended state observer (LESO) is the key link for real-time estimation and on-line compensation of the total disturbances of the system. Its task is to observe the States and total disturbances of the system according to the input and output data. In the drum water level control system [27], a serial three-impulse controller is composed of three signals of drum water level Y(s), steam flow D(s) and feed water flow W(s). The transfer function model of the drum water level system is shown in Fig. 2. In order to compensate for the internal disturbance and external disturbance to the control system by feedforward method, the mathematical model of the water level system can be simplified to a second-order system, i.e. n = 2, using LESO correlation description: €y ¼ f ð_y; y; wÞ þ b0 u;

ð1Þ

where, y is the output signal of the system, u is the control input signal of the system, b0 is the gain of the controller, w is the external disturbance of the system [28]. f (
) is the total disturbance of internal disturbance plus external disturbance. Define the state, x1 ¼ y; x2 ¼ y_ ; x3 ¼ f and the total perturbation is extended to n + 1 = 3 order linear system, then the state space of Eq. (1) is expressed as:


x_ ¼ Ax þ b0 Bu þ Ef_ ; y ¼ Cx

ð2Þ

where, 2

0 A ¼ 40 0

2 3 3 2 3 0 1 0 0 0 1 5; B ¼ 4 1 5; E ¼ 4 0 5; C ¼ ½ 1 0 1 0 0

0 0 :

Therefore, when the controller gain estimate is greater than zero [29], targeting (2), the following third-order LESO is established: z_ ¼ Az þ ^b0 Bu þ Ke;

ð3Þ

where, e ¼ x  z1 ¼ y  z1 denotes observation errors, and   K ¼ ½k1 k2 k3  ¼ a1 x0 a2 x20 an þ 1 xn0 þ 1 ; aj ¼

ðn þ 1Þ! ; j!ðn þ 1  jÞ!

ð4Þ ð5Þ

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where, k1, k2 and k3 are the gain parameters, the LESO characteristic equation are configured in the following form: k ¼ sn þ 1 þ x0 a1 s2 þ x20 a2 s1 þ xn0 þ 1 an þ 1 ¼ðs þ x0 Þn þ 1 ;

ð6Þ

with this configuration, the number of parameters can be reduced and the difficulty of parameter tuning can be reduced. The n+1 parameters can be simplified to the q parameter, which is called observer bandwidth. On the basis of accurate estimation of the state of the system expansion, with the linear state error feed-back (LSEF), the system can approximately be simplified to a disturbance free form and then control it. The controller is designed as follows:


u0 ¼ kp ðr  yÞ  kd y_ ; u ¼ u0^bz3

ð7Þ

0

Since Z  Y The closed-loop dynamic equation of the system can be approximately written as: €y þ kd y_ þ kp y ¼ kp y;

ð8Þ

where kp and kd are the parameters of the controller. yr is the water level setting value. In order to reduce the number of parameters, the gain parameters x0 of the controller are selected to make kp = x2c and kd = 2xc. The structure diagram of the linear ADRC system [30] for drum water level is shown in Fig. 3. 2.2

Generalized Expansion Observer

Considering SISO nonlinear systems with uncertainties,


x_ ¼ Ax þ Bð^bð xÞ þ ^að xÞu þ Dðx; d ÞÞ ; y ¼ Cx

ð9Þ

where D(
) is the total disturbance depending on the internal state and external disturbance of the system, which includes external disturbance, model parameter perturbation, unmodeled dynamics, system state coupling and so on. Based on the analysis of the equivalent modal of the system, the equivalent disturbance system model is established as follows:


h_ ¼ hðhÞ þ pðhÞl ; D ¼ gð hÞ

ð10Þ

h is disturbance system state, µ is the input of the disturbance system, h and p are two smooth vector fields, and g is a smooth mapping. The perturbation system input is related to the external disturbance d (t) and the system state x, and the relative order of

Active Disturbance Rejection Control of Drum Water Level

195

the perturbation system [31]. Equation (10) is transformed into a linear model by means of a non-linear state transformation.


g_ ¼ Ad g þ Bd d ; y ¼ Cd g

ð11Þ

where, 2 " Ad ¼

Ar 0ðmr þ 1Þr

Cd ¼ ½10


0;

0 1

0




0

3

# 60 0 6 0ðr1ÞðmrÞ ; Ar ¼ 6 6 .. .. 0ðmr þ 1ÞðmrÞ 4. .

1 .. .

" #


07 7 0ðr1Þ1 7 ; . 7; Bd ¼ .. Iðmr þ 1Þ1 . .. 5

0 0

0




1

1 1 r d ¼ Lp Lr1 h g(W ðgÞÞ þ Lh gðW ðgÞÞl:

Considering the system model and disturbance model, the generalized state of the T system is recorded as x ¼ ½xT gT  , and the generalized model of the system is obtained as follows: 8       A BCd 0 B ^ > > _ x ¼ x þ ðbðxÞ þ ^ aðxÞuÞ þ d > > B 0 A 0 > d d < |ffl{zffl} |fflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflffl} |fflffl{zfflffl} ;    B E A > > >    y ¼ ½ C0  ½  ½ 0  x ; D ¼ 0C x ; x ¼ I x d nn > > |{z} |ffl{zffl} |fflfflffl{zfflfflffl} :  Cm

 Cd

ð12Þ

 Cx

according to the reconstructed system model described [32] in Eq. (12), a generalized ESO is designed:


 ^x þ Bð  ^bð^xÞ þ ^að^xÞuÞ þ Lðy  ^yÞ ^x_ ¼ A :   xx ^ ^y ¼ Cmx; ^x ¼ C

ð13Þ

3 GLESO Simulation and Performance Analysis of Drum Water Level Considering the slope disturbance, the first derivative of external disturbance to time is constant, and the first derivative of total disturbance to time is constant in steady state. Define the expansion state of drum water level system as x3 ¼ f ð
Þ; x4 ¼ f_ ð
Þ reconstruct the system, and get the following model:


x_ 1 ¼ x2 ; x_ 2 ¼ x3 þ b0 u ; x_ 3 ¼ x4 ; x_ 4 ¼ hðtÞ; y ¼ x1

ð14Þ

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for the reconstructed system, the generalized state observer GLESO is:


z_ 1 ¼ z2 þ k1 ðy  z1 Þ; z_ 2 ¼ z3 þ k2 ðy  z1 Þ þ b0 u ; z_ 3 ¼ z4 þ k3 ðy  z1 Þ; z_ 4 ¼ k4 ðy  z1 Þ

ð15Þ

considering the periodic disturbance, the sinusoidal external disturbance is taken as an _ ¼ x2 dðtÞ Define the expansion state of the example to satisfy the requirement dðtÞ x3 ¼ f ðÞ; x4 ¼ f_ ðÞ; x5 ¼ €f ðÞ þ x2 dðtÞ system, , reconstruct the system and get the ZY following model:


x_ 1 ¼ x2 ; x_ 2 ¼ x3 þ b0 u ; x_ 3 ¼ x4 ; x_ 4 ¼ x5  x2 x3 ; x_ 5 ¼ hðtÞ ; y ¼ x1

ð16Þ

the generalized state observer GLESO for reconstructed system design is:


z_ 1 ¼ z2 þ k1 ðy  z1 Þ; z_ 2 ¼ z3 þ k2 ðy  z1 Þ þ b0 u; z_ 3 ¼ z4 þ k3 ðy  z1 Þ ; z_ 4 ¼ z5 þ k4 ðy  z1 Þ  x2 z3 ; z_ 5 ¼ k5 ðy  z1 Þ

ð17Þ

the initial water level of the drum is 0 and the water level is set yr = 1. The transfer function and related parameters of the control object of the drum water level control system [33] in a power plant are as follows: GW ðsÞ ¼

0:037 3:045s  0:037 ; GD ðsÞ ¼ ; HD ðsÞ ¼ HW ðsÞ ¼ 0:0174; sð30s þ 1Þ 15s2 þ s

GV ðsÞ ¼ 20; HY ðsÞ ¼ 1: Cascade feedforward coincidence control solution is adopted. LADRC as the main tone, the parameter of LESO are b0 = 0.02, xc = 0.28, x0 = 1.12. The parameters of the PID sub-controller are kp = 1.2, ki = 0.35, kd = 5. When external disturbance is slope disturbance, sub-loop disturbance plus unit slope disturbance at t = 100 s, and main circuit plus unit slope disturbance at t = 200 s. GLES and controller values, b0 = 0.02, xc = 0.28, x0 = 1.12, ½k1 k2 k3 k4 k5    ¼ 5x0 10x20 10x30 5x40 x50 .

Fig. 3. Structure of linear ADRC system for drum water level

Fig. 4. Drum water level under slope disturbance.

Active Disturbance Rejection Control of Drum Water Level

Fig. 5. Drum water level under sinusoidal disturbance of main circuit

197

Fig. 6. Drum water level under sinusoidal disturbance of secondary circuit.

From the simulation results of Fig. 4, GLADRC has better control performance than LADRC, smaller overshoot and shorter adjustment time, regardless of whether the disturbance is added to the secondary circuit or the main circuit. When the external disturbance is periodic, when t = 100 s, the sinusoidal disturbance with amplitude of 1 and x = 1 is added to the main circuit. The simulation results are shown in Fig. 5. When t = 100 s, the sinusoidal disturbance with an additional amplitude of 1, x = 1 is obtained. The simulation results are shown in Fig. 6. When sinusoidal disturbance occurs, the amplitude of periodic disturbance is 1 and the period is 1 rad/s. The first derivative of external disturbance to time is also a sinusoidal signal of the same period. The first derivative of total disturbance to time will change periodically with time. The simulation results show that the output error of the ADRC based on LESO is a sinusoidal signal of the same period, the amplitude of the output error sinusoidal signal is about 0.0075 when the secondary circuit is loaded with sinusoidal disturbance, and the amplitude of the output error sinusoidal signal is about 0.132 when the main circuit is loaded with sinusoidal disturbance. The timevarying disturbance of the ADRC based on LESO can’t be estimated completely, and the control error changes periodically. When the disturbance contains sinusoidal components, GLESO can effectively reconstruct the system model using the prior information of disturbance. The reconstructed model reflects the information of periodic disturbance. Therefore, when the disturbance enters the steady state, the observer estimation error can converge to 0, and the error of the ADRC drum water level control system [34] based on GLESO can also converge to 0.

4 Conclusions Control effect of drum water level will affect the safe, stable and economic operation of the whole unit [35]. Due to large and time-varying disturbance, ADRC strategy is presented in this paper for drum water level control, realized with traditional ESO and LADRC. The control effect for constant external disturbances is very satisfactory, but the time-varying external disturbances cannot be fully estimated and compensated with some sinusoidal estimation errors and output errors. Furthermore, the ADRC system based on GLESO is studied, and the model of the system is reconstructed by the prior

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information of disturbance, reflecting the information of periodic disturbance. Results show that the output overshoot is small, the adjustment time is short, the antiinterference ability is strong, and the control precision is high, which is more appropriate for engineering applications. Acknowledgements. This work was partially financially supported by National Natural Science Foundation of China under grants 61533012 and 91748120.

References 1. Chen RM, Zhang X, Zhang WD (2017) Boiler water level estimation method based on adaptive EKF filtering algorithm. Control Eng 24(2):293–296 (in Chinese) 2. Hu CM, Ren J (2014) Study and application of LADRC for drum water-level cascade threeelement control. China Power 47(12):28–31 (in Chinese) 3. Liu XY, Feng LQ (2018) Application of extended Kalman neural network in boiler drum water level control. Mod Electron Technol 41(19):96–99 (in Chinese) 4. Li J, Wang XG (2015) Model reference adaptive inner model-based control in boiled drum water level based on fuzzy-PID control. Therm Power Gener 44(2):96–100 (in Chinese) 5. Rong PX, Gao SY, Wang ZX (2015) The optimization of drum water level control system for industrial boiler. J Harbin Univ Sci Technol 20(6):78–82 (in Chinese) 6. Han JQ (1998) ADRC and its applications. Control Decis 13(1):19–23 (in Chinese) 7. Han JQ (2008) ADRC technique - a control technique that estimates and compensates for uncertainties. National Defense Industry Press, Beijing (in Chinese) 8. Han J (2009) From PID to active disturbance rejection control. IEEE Trans Ind Electron 56 (3):900–906 9. Fu CF (2018) Analysis and design of linear ADRC. PhD thesis, North China Electric Power University. (in Chinese) 10. Xia Y, Liu B, Fu M (2012) Active disturbance rejection control for power plant with a single loop. Asian J Control 14(1):239–250 11. Wang Y, Yao Y, Ma K (2008) A new type extended state observer for system with measurement noise. In: IEEE international conference on automation and logistics, pp 1745– 1749 12. Han JQ (1995) An extended state observer for a class of uncertain objects. Control Decis 10 (1):85–88 (in Chinese) 13. Chen ZK, Wang YT, Zhang RC (2012) An improved mixed gas pipeline multivariable decoupling control method based on ADRC technology. J Comput 7(9):2248–2255 14. Su JB (2015) Robotic un-calibrated visual serving based on ADRC. Control Decis 30(1):1–8 (in Chinese) 15. Su JB, Qiu WB, Ma HY et al (2004) Calibration-free robotic eye-hand coordination based on an auto disturbance-rejection controller. IEEE Trans Robot 20(5):899–907 16. Talole SE, Kolhe JP, Phadke SB (2010) Extended-state- observer-based control of flexiblejoint system with experimental validation. IEEE Trans Ind Electron 57(4):1411–1419 17. Fu YM, Wang YH, Guo F (2018) Design of linear active disturbance rejection controller for drum water level of heat recovery boiler. Therm Power Eng 33(10):83–89 (in Chinese) 18. Jiang JG, Liu YQ, Guo ML (2016) Linear auto disturbance rejection control system for water level in drum. Therm Power Gener 45(7):110–114 (in Chinese) 19. Chen ZQ, Cheng Y, Sun QL (2017) Surveys on theory and engineering applications for linear active disturbance rejection control. Inf Control 46(3):257–266 (in Chinese)

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20. Gao ZQ (2013) On the foundation of active disturbance rejection control. Control Theory Appl 30(12):1498–1510 (in Chinese) 21. Li J, Qi XH, Wan H, Xia YQ (2017) Active disturbance rejection control: theoretical results summary and future researches. Control Theory Appl 34(3):1–10 (in Chinese) 22. Mei Y, Huang LP (2009) A second-order auto disturbance rejection controller for matrix converter fed induction motor drive. In: IEEE 6th international power electronics and motion control conference, pp 1964–1967 23. Gao ZQ (2003) Scaling and bandwidth-parameterization based controller tuning. Proc Am Control Conf 6:4989–4996 24. Gao ZQ (2006) Active disturbance rejection control: a paradigm shift in feedback control system design. In: Proceedings of the American control conference, p 2399C2405 25. Zhao S, Gao ZQ (2010) Active disturbance rejection control for non-minimum phase systems. In: Proceedings Chinese control conference, pp 6066–6070 26. Wang L (2015) Observer-based disturbance rejection control methodology and performance evaluation. PhD thesis, Shanghai Jiao Tong University. (in Chinese) 27. Zhou L, Sun X (2011) The study of boiler control system of water level of steam drum based on new immune PID controller. In: 2nd international conference on digital manufacturing & automation, pp 1336–1339 28. Shao D, Xu SC, Du AM (2018) Absolute stability analysis of nonlinear active disturbance rejection control for electromagnetic valve actuator system via linear matrix inequality method. Proc Inst Mech Eng, Part I: J Syst Control Eng 232:1134–1145 29. Miklosovic R, Radke A, Gao ZQ (2006) Discrete implementation and generalization of the extended state observer. In: American control conference, pp 2209–2214 (2006) 30. Tian G, Gao ZQ (2007) Frequency response analysis of active disturbance rejection based control system In: IEEE international conference on control applications, pp 1595–1599 (2007) 31. Xiang GF, Huang Y, Yu JR et al (2018) Intelligence evolution for service robot: an ADRC perspective. Control Theory Technol 16(4):324–335 (in Chinese) 32. Zhang YJ, Zhang J, Wang L et al (2016) Composite disturbance rejection control based on generalized extended state observer. ISA Trans 63:377–386 33. Liu S, Zhao SQ, Wang YC (2016) Smooth sliding mode control and its application in ship boiler drum water level. Math Probl Eng 1–7 34. Sheng L, Zhao SQ, Wang YC (2015) Sliding mode controller with aga for drum water level of ship boiler. In: 34th Chinese control conference (CCC), pp 3116–3120 (2015) 35. Hu LJ, Zhang K, Liu T (2012) Study on the boiler drum water level based on fuzzy adaptive control. In: 24th Chinese Control and Decision Conference (CCDC), pp 1659–1663 (2012)

Improved Cuckoo Search Algorithm Based on Exponential Function Kun Wang(&), Xiaofeng Lian, and Bing Pan College of Computer and Information Engineering, Beijing Technology and Business University, Beijing, China [email protected]

Abstract. The Cuckoo Search Algorithm (CS) is a novel swarm intelligence optimization algorithm inspired by biology. An improved cuckoo algorithm with adaptive adjustment of discovery probability and step size control factor is proposed to solve the problem of slow convergence speed and low calculation accuracy of CS algorithm. The algorithm uses exponential curves to simulate the changing trend of a and Pa , and establishes the dynamic adjustment model of the two parameters mentioned above, so as to effectively improve the global search ability in the initial stage of iteration and accelerate the local search in the later stage of iteration to achieve stable convergence. Finally, the cuckoo algorithm and improved cuckoo algorithm are compared and analyzed under several common benchmark functions. The experimental results show that the cuckoo algorithm of exponential curve adaptive parameter model converges faster and calculates more accurately. Keywords: Cuckoo Search Algorithm
Adaptive adjustment of discovery probability Exponential curve


Adaptive step control factor


1 Introduction In recent years, intelligent optimization algorithm has been widely studied in computer, control engineering, electrical power, biological science, artificial intelligence and other fields. It has achieved rapid development. The accuracy, convergence speed, stability and computational complexity of the intelligent optimization algorithm are usually the main factors to consider whether the algorithm can be effectively applied, and these factors are closely related to the parameter setting of the algorithm [1, 2]. Swarm intelligence algorithm discovers and reveals the activity law of biological groups through a large number of observations in the environment, such as foraging, evading pursuit or survival positioning, to inspire people to optimize the solution of practical problems. For example, the commonly used intelligent optimization algorithms: ant

This work was funded by the National Natural Science Foundation of China grant number (61702020), Beijing Natural Science Foundation grant number (4172013) and Beijing Natural Science Foundation-Haidian Primitive Innovation Joint Fund grant number (L182007). © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 200–207, 2020. https://doi.org/10.1007/978-981-32-9050-1_23

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colony algorithm and particle swarm optimization algorithm are inspired by the foraging behavior of ant colony and bird colony. Cuckoo Search Algorithm (CS) is a new biologically inspired swarm intelligence optimization algorithm [3, 4]. The algorithm is inspired by the parasitic behavior of cuckoos. On the basis of long-term study of cuckoo’s living habits, it mainly uses cuckoo’s nesting and spawning behavior and Levy’s flight characteristics. The cuckoo search algorithm has good adaptability, fewer parameters, strong search ability, high randomness, excellent global search and local search ability [6]. The advantages of cuckoo search algorithm obviously bring about extensive research and application. In document [4, 5], Yang and Rajabioun, as the proponents of the algorithm, use cuckoo algorithm to optimize engineering, multi-objective optimization problems are also applied in practical engineering. In document [7], Walton et al. introduced the method of exchanging information into the algorithm, which improved the randomness of the algorithm and the random step size of cuckoo search algorithm, but decreased the global search ability. In document [10], Milan TUBA and others adaptively adjust the discovery probability and step size control factor of cuckoo algorithm. In document [11], Fan et al. added Gauss perturbation to the nest position during the cuckoo algorithm iteration, which significantly improved the convergence speed, but the stability of the algorithm declined in the later period. In document [12, 13], Shih et al. introduced Markov chain into cuckoo algorithm, which significantly improved the global search ability of the algorithm, but Markov chain made the algorithm converge slowly in the later stage and affected the global convergence. In document [14, 15], Jin et al. proposed that cuckoo search algorithm should be re-grouped to achieve pre-set step size, which can significantly improve the search performance, but there is a lot of randomness and increase the computing time.

2 CS Algorithm Random or quasi-random walk strategy is adopted in foraging of animals in nature, which is only related to the current location and transfer probability when looking for the next location. Levy’s flight was named after mathematician Paul Levy, who borrowed the animal foraging process for its realization. The flight mechanism has obvious characteristics, strong randomness, and the step size obeys power law distribution and Markov process. Because Levy’s flight has ideal characteristics, Cuckoo’s global search uses this flight mechanism. Cuckoo flight process is a random process. The current flight trajectory will be different from the previous moment. The direction and step size are random in the flight process. It will not stay in one place all the time during the whole flight process. The implementation of CS algorithm needs to satisfy the following three conditions: Route selection is random, cuckoos will find the best nest to complete parasitism; Bird’s nests can continue to be used and the best hatched ones can be retained; The number of parasitic host nests remains the same during the implementation of the algorithm, and the probability of eggs being found is Pa .

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Under idealized conditions, the Cuckoo’s position update formula is as follows: xi ðt þ 1Þ ¼ xi ðtÞ þ LðkÞ  a

i ¼ 1; 2;


; n

ð1Þ

xi ðt þ 1Þ is the position of the i th bird’s nest in the t þ 1 th generation; xi ðtÞ is the position of the i th bird’s nest in the t th generation. a represents the proportional step factor, LðkÞ stands for random search trajectory, and random step obeys the following distribution: levy  u ¼ tk

1\k  3

ð2Þ

t indicates random step size. The above are the specific implementation steps of cuckoo parasitic behavior, Levy flight and cuckoo search algorithm. Next, an improvement based on cuckoo algorithm is proposed to improve the accuracy and convergence speed of the algorithm.

3 Cuckoo Search Algorithm Based on Exponential Function 3.1

Principle of ECS Algorithm

In the CS algorithm, it is found that both the probability and step size control factors are fixed parameters, and the two values can’t be changed during the iteration process, so the selection is passive and mechanical. An adaptive cuckoo algorithm (ECS) based on exponential curve model is proposed according to the change rule of discovery probability and step size control factor in the iteration process. As the relevant parameters in the improved cuckoo search algorithm, the a should be large enough in the initial stage of the algorithm, so as to avoid premature maturity of the algorithm and facilitate the algorithm to find the global optimal solution faster. In order to speed up the convergence speed, the a should be reduced faster, that is accelerated decline; in order to find the local optimal solution and the stability of the algorithm, the a should be reduced slowly later, that is decelerated decline. In order to maintain a strong global search ability initial, The Pa should be relatively small. In order to speed up convergence, The Pa should be increased faster initial, that is accelerated rise. And then, in order to maintain a strong local search ability, The Pa should be relatively large, The Pa should be increased slowly later, that is decelerated rise. The maximum of a is amax , and the minimum of a is amin . As the number of iterations increases, the a can be expressed by the following equation: a ¼ amin þ ðamax  amin Þ expðatÞ

ð3Þ

The t is the number of iterations, when t = 0, a ¼ amax .when t tends to infinity, a ¼ amin . The a can adjust the curvature of the curve, which means adjust the falling speed of the curve. The a can be accelerated and then decelerated in the function

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definition domain, which meets the requirements of the improved cuckoo algorithm for a. The exponential function model can be used to construct the expected a. The maximum of Pa is Pamax , and the minimum of Pa is Pamin . As the number of iterations increases, the a can be expressed by the following equation: Pa ¼ Pamax  ðPamax  Pamin Þ expðatÞ

ð4Þ

The t is the number of iterations, when t = 0, Pa ¼ Pa min .when t tends to infinity, Pa ¼ Pa max . The a can adjust the curvature of the curve, which means adjust the falling speed of the curve. The Pa can be accelerated and then decelerated in the function definition domain, which meets the requirements of the improved cuckoo algorithm for Pa . The exponential function model can be used to construct the expected Pa . 3.2

Implementation of ECS Algorithm

The steps of ECS algorithm implementation are as follows: Step1. The parameters of the algorithm are set as follows: maximum and minimum of step size control factor, maximum and minimum of probability, upper and lower bounds of independent variables and iteration times of the algorithm. Step2. The random initial population is generated, and the objective function value of each nest is calculated, leaving the current optimal value. Step3. If the result satisfies the end condition, the output result is the optimal position and corresponding optimal value. If the end condition is not met, step 4 is executed. Step4. Formula 4 and 5 are used to calculate the values after each iteration. Other nests are updated according to Formula 1. Step5. Increase the number of iterations one time, and go to step 2.

4 Experimental Simulation and Analysis Through simulation experiments, the performance of cuckoo search algorithm (ECS) and traditional cuckoo algorithm are tested and compared. This paper chooses six standard functions to the test, including Sphere, Griewank, Rastrigin, Ackley, Rosenbrock and Schaffer. The parameter settings of each benchmark function are shown in Table 1. It is known from reference that the algorithm can approach the optimal solution faster when it is equal to 1 and faster when it is equal to 0.75. Therefore, in the experiment, the range of values is [0, 1], and the range of values is [0.1, 0.75]. Cuckoo algorithm and improved cuckoo algorithm (ECS) were tested 30 times independently. The number of nests was 20 and the number of iterations was 1000.

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Fig. 1. The convergence curve of Sphere

Fig. 2. The convergence curve of Griewank

Fig. 3. The convergence curve of Rastrigin

Fig. 4. The convergence curve of Ackley

Improved Cuckoo Search Algorithm Based on Exponential Function

Fig. 5. The convergence curve of Rosenbrock

Fig. 6. The convergence curve of Schaffer

Table 1. Dataset used in the experiment. Data set name Formula P Sphere f ðxÞ ¼ di¼1 x2i   Pd 2 Qd xiffi Griewank 1 p þ1 f ðxÞ ¼ 4000 i¼1 xi  i¼1 cos i  Pd  2 Rastrigin f ðxÞ ¼ 10  ðdÞ þ i¼1 xi  10 cosð2pxi Þ " rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi# Ackley 1 Xd 2 x f ðxÞ ¼ 20 exp 0:2 i¼1 i d  X  1 d cosð2px Þ þ 20 þ e  exp i i¼1 d h i P 2 Rosenbrock 2 2 f ðxÞ ¼ d1 i¼1 ð1  xi Þ þ 100ðxi þ 1  xi Þ P  d Schaffer 2 sin2 i¼1 xi  0:5 f ðxÞ ¼ 0:5 þ  P 2 d 2 1 þ 0:001 i¼1 xi   þ jxn  1j 1 þ sin2 ð3pxn Þ

Dimension Range 20 [− 100, 100] 20

[− 600, 600]

20

[− 5.12, 5.12]

20

[− 32, 32]

20

[− 100, 100]

20

[− 500, 500]

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K. Wang et al. Table 2. Comparison table of optimal values of each algorithm Data set name Sphere Griewank Rastrigin Ackley Rosenbrock Schaffer

CS 0.00008358 0.00624210 68.1289711 0.01238645 0.01831563 0.00004539

ECS 0.00001807 0.01246222 19.7324126 0.00022785 0.00712321 0.00001376

The comparison of two kinds of algorithms under Table 2 Sphere function shows that the optimal value of ECS algorithm under single-peak separable function is better than that of CS algorithm, and ECS algorithm is close to the theoretical optimum. From Fig. 1, we can see that the convergence speed of ECS algorithm is faster than that of CS under single-peak separable function, and at the same time it tends to be more stable. Griewank and Rastrigin functions are complex, multi-peak and separable highdimensional functions. The comparison between the two kinds of algorithms shows that under Griewank and Rastrigin functions the optimum value of ECS algorithm is better than CS algorithm under Table 2 From Figs. 2 and 3, we can see that the convergence speed of ECS algorithm is faster than CS under multi-peak separable function, and it tends to be more stable at the same time. Ackley and Rosenbrock are high-dimensional functions with complex multimodal indivisibility. Comparing the two algorithms in Table 2, Ackley and Rosenbrock, we can see that the optimal value of ECS algorithm under multimodal indivisible function is better than that of CS algorithm. From Figs. 4 and 5, we can see that the convergence speed of ECS algorithm under multimodal indivisible function is faster than that of CS, and at the same time it tends to be more stable. There are many local optimum values in the search domain. The comparison of two kinds of algorithms under Schaffer function, the optimal value of ECS algorithm is better than CS algorithm under this condition in Table 2. From Fig. 6, it can be seen that the convergence speed of ECS algorithm is faster than that of CS algorithm in multi-peak and non-separable low-dimensional.

5 Conclusion Based on the traditional cuckoo algorithm, an adaptive cuckoo algorithm based on exponential curve model is proposed in this paper. By using exponential function model to dynamically adjust step size control factor and discovery probability, the dependence of cuckoo algorithm on relevant parameters is well controlled. The adaptive cuckoo algorithm in this paper can adjust the step size control factor and discovery probability adaptively. Experiments using typical test functions show that the proposed ECS algorithm has higher accuracy and faster convergence speed than the traditional cuckoo algorithm. According to this paper and references, it can be observed that cuckoo algorithm and its improved algorithm are applied in many fields, and the research prospects are very broad. Next step, we can conduct in-depth research on

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algorithm fusion, promote the development and application of this algorithm, and further study on the application of CS algorithm in the future.

References 1. Srivastava PR, Khandelwal R, Khandelwal S et al (2012) Automated test data generation using cuckoo search and tabu search algorithm. Journal of Intelligent Systems 21(2):195– 224 2. Chen T (2009) A simulative bionic intelligent optimization algorithm: artificial searching swarm algorithm and its performance analysis. In: The second international joint conference on computational science and optimization, pp 864–866 3. Yang XS, Deb S (2013) Multiobjective cuckoo search for design optimization. Comput Oper Res 40(6):1616–1624 4. Yang XS, Deb S (2010) Engineering optimization by cuckoo search. Int J Math Model Num Optim 4:330–343 5. Yang XS, Deb S (2009) Cuckoo search via Levy flights. In: Proceedings of world congress on nature & biologically inspired computing. IEEE Publications, pp 210–214 6. Brownlee J (2011) Clever algorithms: nature-inspired programming recipes. http://lulu.com 7. Walton S, Hassan O, Morgan K et al (2011) Modified cuckoo search: a new gradient free optimisation algorithm. Chaos Solitons Fractals 44(9):710–718 8. Rajabioun R (2011) Cuckoo optimization algorithm. Appl Soft Comput 11:5508–5518 9. Valian E (2011) Improved cuckoo search algorithm for global optimization. Int J Commun Inf Technol 1(1):31–44 10. Tuba M, Subotic M, Stanarevic N (2011) Modified cuckoo search algorithm for unconstrained optimization problems. In: European conference on European computing conference. world scientific and engineering academy and society (WSEAS), pp 263–268 11. Fan W, Xingshi H, Yan W (2011) Cuckoo search algorithm based on Gauss perturbation. J Xi’an Univ Eng 25(4):566–569 12. Wen S, Wei H, Ruiwen D (2017) Cuckoo algorithm for job shop scheduling problem under simulated annealing. Comput Eng Appl 53(17):249–253 13. Fan W, Xingshi H, Yan W (2012) Markov model and convergence analysis based on CS algorithms. Comput Eng 38(11):180–182 14. Li X, Yin M (2016) A particle swarm inspired cuckoo search algorithm for real parameter optimization. Soft Comput 20(4):1389–1413 15. Liu X, Fu M (2015) Cuckoo search algorithm based on frog leaping local search and chaos theory. Appl Math Comput 266(C):1083–1092

Autonomous Intelligent Control for Path Following of Unmanned Surface Vessels with Input Constraints Yalei Yu1, Chen Guo2(&), and Haomiao Yu2 1

The School of Navigation, Dalian Maritime University, Dalian 116026, China [email protected] 2 The School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China [email protected], [email protected]

Abstract. External disturbances and input saturation are considered in this work, presenting a velocity-varying line-of-sight and finite-time disturbance observers based backstepping (VLFDOB) control scheme to realize unmanned surface vessels’ (USVs) path following control. We explore this scheme of combining velocity-varying line-of-sight (VLOS) guidance laws and finite-time disturbance observers with designing auxiliary dynamic systems in order to produce guidance heading angle and cope with disturbances acting on USVs as well as anti-windup simultaneously. The performance of stability of this scheme is shown with effective and accurate performance through Lyapunov stability theories. Simulating results demonstrate that the proposed scheme enables USVs’ system to reach desired path following objectives. Keywords: Path following


Observers
Input saturation
Line-of-sight

1 Introduction The problem of path following with a predefined parametric path for unmanned surface vessels (USVs) is one of the most crucial issues in the development of the autonomous shipping industry [1]. For this topic, which has attracted an enormous amount of attention over the last decades, and generally, the main problems appearing in this topic that need to be addressed necessarily are the design of guidance scheme in kinematics levels and the construction of controllers in kinetics levels [2, 3]. The well-known guidance schemes that have been investigated in the areas of vessels’ motion control are line-of-sight (LOS) and its various improved forms [1, 4, 5]. Other problems associated with reaching control objectives of path following of USVs are that how to cope with external disturbances and how to compensate for input constraints due to the limitation of mechanical properties. The former problem could be solved by introducing adaptive control, and there are many achievements achieved by this way [6, 7]. How to build effective auxiliary systems is considered normally through two ways that are developing auxiliary systems acting on control inputs directly and acting on system

© Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 208–215, 2020. https://doi.org/10.1007/978-981-32-9050-1_24

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states to ensure that the control inputs satisfy the limitation of practical mechanical systems respectively [3, 8]. Inspired by the above statements, depending on previous works [6, 7], finite-time disturbance observers that are used to cope with external disturbances and auxiliary dynamics that are introduced to compensate for USVs’ system input constraints are considered simultaneously in this paper.

2 Problem Formulation The mathematical model of a USV when it is moving on a horizontal plane, is described as follows [1] (

M v_ ¼  CðvÞv  DðvÞv þ s þ dd g_ ¼J ðwÞv

ð1Þ

where g ¼ ½ x y w T denotes the position ðx; yÞ and the attitude w of USVs in the earth-fixed frame. The velocity vector v ¼ ½ u v r T describes the surge velocity u, the sway velocity v and the yaw rate r in the body-fixed frame of USVs. The control inputs s ¼ ½ su 0 sr T contain the surge force su and the yaw moment sr . ½ dd;u dd;v dd;r T is on component form of dd and represents the external disturbances caused by waves, wind and ocean currents. The system inertia matrix M, Coriolis-centripetal C, damping matrix D and rotation matrix J are given by literature [1, 9]. For the convenience of designing finite-time disturbances observers and stability analysis, the dynamic model of this USV can be rewritten as follows (

g_ ¼ J ðwÞv v_ ¼ gðvÞ þ s0 þ d0 d

ð2Þ

where gðvÞ ¼ M 1 ðC þ DÞ, s0 ¼ M 1 s and d0 d ¼ M 1 dd . In this paper, control laws s are constrained by the saturation values smax ¼ ½ sumax 0 srmax T . Therefore, the control inputs s can be expressed as follows 8 si0 [ simax > < simax ; si ¼ satðsi0 Þ ¼ si0 ;  simax  si0  simax > :  simax ; si0 \  simax Control objective:

  sup x  xp   ex ,

t2½t0 ;1

  sup y  yp   ey and

t2½t0 ;1

ð3Þ

sup ku  ud k

t2½t0 ;1

 eu , where ex , ey and eu are small positive constants, and ud is the desired surge velocity designed later.

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3 Preliminaries Lemma 1 [10]. The following system r_ 0 ¼ k0 L1=n þ 1 jr0 jn=ðn þ 1Þ signðr0 Þ þ r1 .. .

r_ n1 ¼ kn1 L1=2 jrn1  r_ n2 j1=2 signðrn1  r_ n2 Þ þ rn r_ 0 2 kn Lsignðrn  r_ n1 Þ þ ½L; L

ð4Þ

where L [ 0 and ki ði ¼ 0; 1; . . .; nÞ are appropriate constants, is finite-time stability.

4 Velocity-Varying LOS Guidance Law 4.1

Guidance Subsystem

The velocity-varying LOS (VLOS) guidance law is presented to help USVs reach desired heading   angles which make it possible for USVs to follow a reference path xp ðhÞ; yp ðhÞ . It is clear that for any point of USVs at this path, the position errors xe ¼ x  xp and ye ¼ y  yp can be shown in the path-tangential reference frame as follows 

xe ye



  cos cp   ¼ sin cp 

  T    sin cp x  xp   y  yp cos cp

ð5Þ

  where cp ¼ atan2 y0p ðhÞ; x0p ðhÞ :¼ ½p; p. By differentiating xe and ye with respect to time, we have (

  x_ e ¼U cos w  cp þ b þ ye c_ p  utot   y_ e ¼U sin w  cp þ b  xe c_ p

ð6Þ

pffiffiffiffiffiffiffiffiffiffiffiffiffiffi u2 þ v2 and 0\Umin  U  Umax . The term b :¼ atan2ðv; uÞ repqffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi resents sideslip angles of USVs and utot : ¼ h_ y0p2 ðhÞ þ x0P2 ðhÞ.

where U ðu; vÞ :¼

4.2

Velocity-Varying LOS Design

The time-varying surge velocity guidance law is developed as ud ¼ k k [ 0 and ju0 j denotes the minimum velocity of USVs.

pffiffiffiffiffiffiffiffiffiffiffiffiffiffi u20 þ y2e where

Remark 1. It is reasonable to understand that the maximum velocity of USVs should be bounded, satisfying its practically mechanical performance, i.e., 0\ku0  ud  umax \1. The surge velocity can be expressed as follows

Autonomous Intelligent Control for Path Following of Unmanned Surface Vessels

8 ud [ umax > < umax ; ud ¼ satðud Þ ¼ ud ; ku0 \ud \umax > : ku0 ; ud \ku0

211

ð7Þ

The velocity-varying LOS (VLOS) guidance heading angle is designed as follows 0

1

Dye B C wd ¼ cp ðhÞ þ arcsin@qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiA  b 2 1 þ ðDye Þ

ð8Þ

Remark 2. The desired heading angle wd can be precisely tracked by the actual heading angle w, i.e., satisfying w ¼ wd . According to the Eq. (6), virtual control law is designed as follows   utot ¼ k1 xe þ U cos w  cp þ b

ð9Þ

where k1 is a positive design constant.

5 Finite-Time Disturbance Observers Design Lemma 2. Consider a USV is described by (2), a finite-time disturbance observers designed as follows

ð10Þ

0 0 where Ld ¼ diagðlu ; lv ; lr Þ, ðkd0 ; kd1 Þ 2 R2þ , and b d d is the estimation of dd , which can 0 0 0 d d  dd , identify the external disturbances dd exactly within finite-time, i.e., bv  v and b 8t [ Td . 0 0 0 d d  dd . Differentiating these Proof. Define the following errors ev ¼ bv  v and e dd ¼ b errors with respect to time along (2) and (10) yields

ð11Þ 0 Using Lemma 1, errors ev and e d d can converge to zero in finite time by designed finitetime disturbance observers, i.e., there exists a finite setting time Td so that ev ðtÞ  0 and 0 e d ðtÞ  0, 8t  Td . This concludes the proof.

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6 Control Laws Design Define error variables we :¼ w  wd , re :¼ r  rd and ue :¼ u  ud where rd ¼ k2 we þ w_ d , and kw 2 R þ . Considering the constraint of actuators of USVs, nominal control laws are built as follow 8   > < su0 ¼  ku ue þ gu ðvÞ  u_ d þ ^d0du  ku0 vu   ð12Þ > : sr0 ¼  kr re þ we þ gr ðvÞ  r_ d þ ^ d0dr  kr0 vr where ðku ; kr ; ku0 ; kr0 Þ 2 R4þ . For the convenience of analyzing constraint effect caused by actuators’ input saturation, designing auxiliary system is given as follow 8 jue Dsu j þ 0:5q2u Ds2u > > > hðvu Þ þ qu Dsu < v_ u ¼  kvu vu  vu ð13Þ 2 2 > > > v_ r ¼  kvr vr  jre Dsr j þ 0:5qr Dsr hðvr Þ þ qr Dsr : vr   where kvu ; kvr ; qu ; qr 2 R4þ , Dsi ¼ si  si0 and 8  v 0; v j j > i a > >

2  < p p vi  v2a sin 1  cos ; va \jvi j\vb , satisfying vb [ va [ 0. > 2 2 v2b  v2a > > : 1; jvi j  vb

hðvi Þ ¼

7 Stability Analysis Theorem 2. The uniformly ultimately bounded stable of USVs’ system is guaranteed by combining with VLOS guidance laws (7)–(9), control laws (14), and auxiliary dynamic systems (15), as well as disturbance observers (12). Proof. Choose the complete LFC V ¼ 12 x2e þ 12 y2e þ 12 w2e þ 12 u2e þ 12 re2 þ 12 v2u þ 12 v2r Differentiating V along (6)–(9) and (12)–(13) with respect to time for all t [ Td yields



  1 2 2  2 2 _V ¼  k1 x2e  ky y2e  k2 w2e  ku  1 ku0 ue  kr  kr0 re  kvu  1 v2u 2 2   1 1 ð14Þ  kvr  1 v2r  q2u Ds2u ðhðvu Þ  1Þ  q2r Ds2r ðhðvr Þ  1Þ 2 2  jue Dsu j½hðvu Þ  sgnðue Dsu Þ  jre Dsr j½hðvr Þ  sgnðre Dsr Þ DU ffi \1. where 0\ky :¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 1 þ ðDye Þ

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For hðvu Þ ¼ 1 and hðvr Þ ¼ 1, the above Eq. (14) can be simplified as



 1 2 1 2 2 V_ ¼  k1 x2e  ky y2e  k2 w2e  ku  ku0 u2e  kr  kr0 re 2 2      kvu  1 v2u  kvr  1 v2r   2H1 V

ð15Þ



2 2 where H1 ¼ min k1 ; ky ; k2 ; ku  12 ku0 ; kr  12 kr0 ; kvu  1; kvr  1 . Otherwise, for hðvu Þ\1 and hðvr Þ\1, using Young’s inequality again, inequalities ue Dsu  12 u2e þ 12 Ds2u and re Dsr  12 re2 þ 12 Ds2r could be resulted, and bring these inequalities into (14) yields



 1 2 1 2 1 2 2 _V ¼  k1 x2e  ky y2e  k2 w2e  ku  1 ku0  ue  kr  kr0  re 2 2 2 2      2   2 1 2 1  kvu  1 vu  kvr  1 vr þ Dsu 1 þ q2u þ Ds2r 1 þ q2r 2 2   2H2 V þ 1

2 2 where H2 ¼ min k1 ; ky ; k2 ; ku  12 ku0  12 ; kr  12 kr0  12 ; kvu  1; kvr  1     1 ¼12 Ds2u 1 þ q2u þ 12 Ds2r 1 þ q2r . Taking (15) and (16) together, the following results are obtained as follows V_   2HV þ 1

ð16Þ

and

ð17Þ

2 2 where H ¼ minfH1 ; H2 g, satisfying ku  12 ku0  12, kr  12 kr0  12, kvu [ 1 and kvr [ 1. Thus, according to mathematical calculation, further results from (17) can be produced as follows

 1  2Hðtt0 Þ 1 V ðtÞ  V ðt0 Þ  þ e 2H 2H

ð18Þ

Therefore, it is reasonable to conclude that all error signals in this USV system are uniformly ultimately bounded stable throughout the whole period of path following. This completes proof.

8 Simulation Studies and Results To demonstrate performance and effectiveness of the designed VLFDOB scheme, simulation studies are conducted using a USV whose model is given by (2) and its parameters are stated in reference [9]. The input saturation constraint is set as s ¼ ½ 3N 0 3Nm T . The design parameters are chosen as k1 ¼ 1, k2 ¼ 15, k ¼ 0:5, u0 ¼ 0:2, umax ¼ 0:3, D ¼ 0:5, ku ¼ 18, kr ¼ 17, va ¼ 0:1, vb ¼ 1, kvu ¼ 15, kvr ¼ 15, qu ¼ 0:5, qr ¼ 5, ku0 ¼ 5, kr0 ¼ 5, kd0 ¼ 0:1, kd1 ¼ 0:02 and Ld ¼ diagð200; 200; 200Þ. Initial conditions

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of this USV are set as ½ xð0Þ yð0Þ wð0Þ T ¼ ½ 0 20 p=3 T and ½ uð0Þ vð0Þ r ð0ÞT ¼½ 0 0 0 T . The desired path is defined as xp ðhÞ ¼ h and yp ðhÞ ¼ 100 sinð0:05hÞ. In order to analyze the performance of finite-time disturbance observers, external disturbances are set as dd ¼ ½ 2 sin ð0:05p tÞ 0:1 sin ð0:01p tÞ 2 cos ð0:03p tÞ T . The comparison simulation between VLFDOB without considering input saturation that is marked as Without Sat and VLFDOB with considering input saturation that is marked as With Sat. The results are plotted in Fig. 1a–d, which illustrate effective performance of this scheme. From Fig. 1a, the similarly accurate tracking performance

a

b

c

d

Fig. 1. Simulation results. a. The path following performance. b. Position errors xe and ye , and the heading angle error we . c. The estimation of external disturbances dd;u and dd;r . d. The control laws su and sr .

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is seen in both these situations, although the latter is exposed to a small range of control inputs. In Fig. 1b and c, it is clear that position errors and the heading angle error converge to a significantly small range around zero, meanwhile external disturbances can be estimated precisely. The last sub-figure, Fig. 1d, shows the obvious comparison between these two simulation results for USVs’ path following.

9 Conclusion A novel VLFDOB control scheme is proposed in this paper to complete path following for USVs in the presence of external disturbances and input constraints, and the stability of USVs’ system and tracking performance are demonstrated by Lyapunov’s theories and ensured by conducting digital simulation respectively. Acknowledgement. This work was supported by the National Natural Science Foundation of China under Grant 51879027, 51579024, 6137114 and 51809028, and in party by the Fundamental Research Funds for the Central Universities under Grant 3132019109.

References 1. Fossen TI (2011) Handbook of marine craft hydrodynamics and motion control. Wiley, Trondheim 2. Skjetne R, Fossen TI, Kokotović PV (2004) Robust output maneuvering for a class of nonlinear systems. Automatica 40(3):373–383 3. Yu Y, Guo C, Yu H (2018) Finite-time predictor line-of-sight–based adaptive neural network path following for unmanned surface vessels with unknown dynamics and input saturation. Int J Adv Robot Syst 15(6):1729881418814699 4. Wang N, Sun Z, Zheng Z, Zhao H (2018) Finite-time sideslip observer-based adaptive fuzzy path-following control of underactuated marine vehicles with time-varying large sideslip. Int J Fuzzy Syst 20(6):1767–1778 5. Lekkas AM, Fossen TI (2014) Integral LOS path following for curved paths based on a monotone cubic Hermite spline parametrization. IEEE Trans Control Syst Technol 22 (6):2287–2301 6. Yu Y, Guo C, Shen H, Zhang C (2018) Integral sliding mode adaptive path-following of unmanned surface vessels with uncertain parameters and time-varying disturbances. In: 2018 37th Chinese control conference (CCC), pp 3051–3056. IEEE 7. Yu Y, Guo C, Shen H, Zhang C (2018) Sliding-mode dynamic surface adaptive path following of unmanned vessels with dynamic uncertainties and disturbances. In: 2018 13th world congress on intelligent control and automation (WCICA), pp 939–944. IEEE 8. Zhu G, Du J, Kao Y (2018) Command filtered robust adaptive NN control for a class of uncertain strict-feedback nonlinear systems under input saturation. J Frankl Inst 355 (15):7548–7569 9. Do KD, Pan J (2006) Underactuated ships follow smooth paths with integral actions and without velocity measurements for feedback: theory and experiments. IEEE Trans Control Syst Technol 14(2):308–322 10. Shtessel YB, Shkolnikov IA, Levant A (2007) Smooth second-order sliding modes: missile guidance application. Automatica 43(8):1470–1476

Investigation on Energy Feedback Potentiality of New Hydraulic Interconnected Energy-Regenerative Suspension Zeyu Sun(&), Ruochen Wang , Xiangpeng Meng and Qiuiming Jiang

,

School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China [email protected]

Abstract. To solve the problem of large energy consumption and energy waste in hydraulic interconnected suspension vibration, a hydraulic interconnected energy feedback suspension system is proposed to recover and reuse the energy, and the energy feedback potentiality of the hydraulic interconnected energyregenerative suspension is studied. The model of hydraulic interconnected energy feedback suspensions system is built by design of the constant current feedback circuit, and then, the energy feedback potentiality of this system is investigated by considering different pavement levels. Under the conditions of the B level and C level pavement input excitation, the energy consumption is simulated and analyzed. The bench test is also carried out under B and C pavement levels. By comparing the experimental and the simulated results, It shows that the hydraulic interconnected power feedback suspension has good energy feedback foreground, which provides ideas for new type suspension design and energy recovery and utilization in vibration system. Keywords: Hydraulic interconnected suspension
Energy recovery Constant current circuit
Feedback prospects
Suspension




1 Introduction Traditional hydraulic interconnected suspensions are widely used in construction machinery and special machinery due to its high handling stability and side rollover resistant capability. However, in practical engineering applications, the working conditions of engineering vehicles are relatively complex, and the corresponding interconnected suspension generates much more energy in the process of damping vibration from road. It becomes more and more significant that how to reduce the energy consumption of the suspension and further recover the vibration energy of the suspension [1]. Nowadays, Hawley [2] first proposed the interconnected suspension system, and explained the various interconnection methods between the suspensions, and determined that the interconnected suspension has the characteristics of preventing the pitch and roll of the vehicle based on different interconnected structures. Lam et al. [3] © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 216–225, 2020. https://doi.org/10.1007/978-981-32-9050-1_25

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described the effects of different control algorithms on the performance of hydraulic interconnect suspensions. Cao [4] studied the potential performance benefits of fluidically coupled passive suspensions were verified by analyzing suspension properties. Guo [5] made a detailed study on the dynamic performance characteristics and mechanism of action of interconnected suspension, and proposed the principle of support constraint minimization. The side rollover resistant capability of the vehicle is improved by the reverse interconnection between hydraulic cylinders. In the above research, whether it is the study of the interconnected suspension structure or the control method, only the impact on the dynamic performance of the suspension is analyzed, and the research on the energy recovery of the hydraulic interconnected suspension is neglected. On suspension energy consumption, Yu [6] analyzed the influence mechanism of driving speed, vehicle quality and road roughness on the energy consumption of traditional suspension, and determined the feasibility of suspension feeding energy. However, the related research mainly focused on traditional suspension, and the energy consumption mechanism of interconnected suspension system has not been analyzed. Kawamoto et al. [7] proposed a hybrid suspension system and added a motor-type energy regeneration damper on the basis of a passive suspension to recycle vibration energy. Asadi [8] used linear DC motor as the energy feeding unit to design the test prototype of the electromagnetic energy feeding suspension, and optimized its structure. In summary, this paper introduces a feed energy unit to the proposed new hydraulic interconnected energy-feedback suspension based on the traditional hydraulic interconnected suspension structure, Based on this, the energy consumption model of the hydraulic interconnected energy-suspension suspension is constructed. The feasibility analysis of the new hydraulic interconnected energy-regenerative suspension is carried out. The potential of the new hydraulic interconnected energy-suspension suspension under different working conditions is studied, and the model validity is verified by carrying out the bench experiment.

2 Energy Consumption Model of the New Suspension 2.1

Energy Consumption Basic Model of the New Suspension

According to the previous researches, the road roughness has the most obvious effect on the suspension energy consumption, and the analysis of the energy loss power of the suspension is simplified based on 1/4 vehicle model. Figure 1(a) shows the 1/4 vehicle model, and the cylinder damping coefficient ceq is the equivalent damping, in addition to the hydraulic cylinder’s own damping, which includes the damping of the rectifier bridge, hydraulic motor, hydraulic piping, reservoir and other hydraulic components such as energy absorbers.

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(a) Model with hydraulic cylinder

(b) Model with a traditional passive damper

Fig. 1. 1/4 vehicle model

Ignoring the tire’s damping coefficient, the following suspension system motion equation can be obtained: m1€x1 þ ceq ð_x1  x_ 2 Þ þ k1 ðx1  x2 Þ ¼ 0

ð1Þ

m2€x2 þ ceq ð_x1  x_ 2 Þ  k1 ðx1  x2 Þ þ k1 ðx2  x3 Þ ¼ 0

ð2Þ

Where m1 and m2 are 1/4 body mass (kg) and the unsprung mass (kg), ceq is an equivalent damping coefficient of a hydraulic cylinder (N ∙ s/m), kt is the tire stiffness (N/m) and k1 is the suspension spring stiffness (N/m). 2.2

Evaluation Index

According to the energy consumption basic model of 1/4 vehicle hydraulic interconnected energy-regenerative suspension, the equivalent damping ceq consumes the most significant vibrational energy, and is mostly dissipated in the form of hydraulic cylinder internal friction and piston friction. Therefore, the energy consumption of springs and tires is ignored in this paper. According to theoretical mechanics, the instantaneous power dissipated inside the hydraulic cylinder is the product of the damping force F(t) and the relative moving speed x_ 2 ðt)  x_ 1 ðt), and the hydraulic cylinder instantaneous power is expressed as: PðtÞ ¼ FðtÞ½x_ 2 ðtÞ  x_ 1 ðtÞ ¼ ceq ½x_ 2 ðtÞ  x_ 1 ðtÞ2

ð3Þ

The vibrational energy dissipated by the hydraulic cylinder over a period of time can integrate the instantaneous dissipated power of the hydraulic cylinder. The Eq. (4) can be obtained by: Z E0t ¼ 0

t

Z PðtÞdt ¼ 0

t

ceq ½x_ 1 ðtÞ  x_ 2 ðtÞ2 dt

ð4Þ

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3 Simulation Analysis of the Suspension Energy Consumption 3.1

Dynamic Analysis of the Hydraulic Interconnected Energy-Regenerative Suspension

In this paper, related vehicle parameters are selected as shown in Table 1, according to vehicle parameters in combination with Eq. (4) to establish the Simulink simulation model of 1/4 vehicle hydraulic interconnected energy-regenerative suspension as shown in Fig. 2, and the road surface ratings are selected respectively. The relevant simulation analysis was carried out through selecting four road grads of A, B, C and D, and different driving speed of 10 m/s, 20 m/s, 30 m/s and 40 m/s. The relationships of the energy consumption of suspension, vehicle speed and the grade of road surface are studied to determine the specific energy consumption of the hydraulic interconnected energy-regenerative suspension. This will lay the foundation for the analysis of the energy characteristics of the suspension. Through the simulation analysis, in the case of B-level road and the speed of 20 m/s, performance indicators of the suspension can be also obtained, shown in Figs. 3 and 4, which is Body acceleration and Suspension working space. Table 1. Vehicle parameters Parameter Sprung mass Unsprung mass Spring stiffness Equivalent damping coefficient Tire stiffness The speed of the car

Mark m1 m2 k1 ceq kt u

Value 345 40.5 17 2 192 20

Unit kg kg kN/m kN ∙ s/m kN/m m/s

Fig. 2. Energy consumption model of 1/4 vehicle suspension

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Fig. 3. Body acceleration

Fig. 4. Suspension working space

Figure 2 shows that the energy dissipation power of suspension has a linear relationship with the driving speed, and the energy dissipation power becomes significantly larger as the road surface roughness increases, when vehicle speed is set to 20 m/s, the average power dissipation of single-side suspension reaches 84–338 watts when driving on B-grade road (better road surface) and C-grade road (general road surface), with a good prospect of energy feedback. And in the process of vehicle driving, the dissipation of suspension vibration energy is also increasing when road unevenness increasing and driving speed becoming faster. Therefore, it is more meaningful for the hydraulic interconnection reclaiming energy suspension to be used in off-road vehicle, engineering vehicles and other vehicles. As is shown in Figs. 3 and 4, compared with the traditional passive interconnection suspension, the performance index of the hydraulic interconnection reclaiming energy suspension does not change much, and the system has a good feasibility. 3.2

Research on Vibration Energy Feedback Potential of Suspension

Compared with the traditional hydraulic passive interconnection suspension, the hydraulic interconnected reclaiming energy suspension can recover some of the vibration dissipation energy of the suspension in addition to ensuring the dynamic performance of the vehicle. In this section, we will study and analyze the vibration energy of the suspension that can be recovered by the hydraulic interconnection reclaiming energy suspension when speed is set to 20 m/s or 40 m/s under the random road of class B and C, in the hydraulic interconnection reclaiming energy suspension combined simulation model, the reclaiming energy power at both ends of the reclaiming energy circuit is regarded as the energy of the final recovery of suspension. The energy consumption of suspension and the reclaiming energy power are shown in Fig. 5.

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(a) Grade B road 20m/s

(b) Grade C road 20m/s

(c) Grade B road 40m/s

(d) Grade C road 40m/s

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Fig. 5. Comparison of energy consumption and recovery of suspension

Figure 5 shows time-domain simulation comparison during driving of the vehicle, as shown in the graph, the energy feedback power is about 35% of the energy dissipation energy of the single-side suspension both in class B and C grade pavement, indicating that the hydraulic interconnection reclaiming energy suspension has a good prospect of energy recovery. According to the root mean square statistics of the reclaiming energy power, the comparison bar chart was obtained, as shown in Fig. 6.

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(a) Grade B road

(b) Grade C road

Fig. 6. Comparison of energy consumption and energy consumption of suspension

4 Research and Analysis on Energy Consumption Experiment of Suspension The experiment was carried out on the MTS320 four-channel wheel coupling road simulator, loading the gravel pavement spectrum of a test field, then a random excitation simulation test will be performed on the test sample vehicle equipped with the hydraulic interconnection reclaiming energy suspension. According to ISO/DIS 8608, taking the road grade for class B and class C, and the driving speed for 20 m/s and 40 m/s, the random road surface spectrum produced by the design is input to the MTS test stand for random input test. When the driving speed is 20 m/s, the time domain response of the system under class B pavement is shown in Fig. 7.

(a) Acceleration time domain of side slope

(b) Vertical acceleration of vehicle body

Fig. 7. Time domain comparison of test results of suspension system

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Table 2. RMS value of suspension system

0.7031

Hydraulic interconnected suspension without the circuit 0.9014

Hydraulic interconnection reclaiming energy suspension 0.7504

0.8757

1.0426

0.9386

Hydraulic passive interconnected suspension Lateral inclination acceleration of grade B road Vehicle body acceleration of grade B road

As is shown in Fig. 7 and Table 2, compared with the hydraulic interconnected suspension without the circuit, vehicle body acceleration RMS was reduced by 9.98% and lateral inclination acceleration RMS was reduced by 16.75%, and compared with the traditional passive hydraulic interconnected suspension, vehicle body performance is close, so that the safety driving and riding comfort of the vehicle cannot be affected too much, and the test result is close to the simulation result. The hydraulic interconnection reclaiming energy suspension under the influence of the constant current control circuit is compared with the passive hydraulic interconnected suspension, its dynamic performance has a slight decline, but the recovery of body vibration energy dissipation is considerable. Figure 8 show the suspension reclaiming energy power under different road grades when the speed are 20 m/s and 40 m/s. According to the Fig. 8, when the speed is 20 m/s, the maximum instantaneous reclaiming energy power of class B and class C are 180 W and 320 W respectively, and when the speed is 40 m/s, the maximum instantaneous reclaiming energy power of class B and class C are 536 W and 1342 W respectively, the maximum average power is 153 W, the energy-feed of suspension is considerable. In addition, under the condition of constant speed, with the increase of road grade, namely the increase of road surface roughness, the reclaiming energy power increases significantly, and this suspension has better energy-regenerative performance (Fig. 9).

(a) 20m/s

(b) 40m/s

Fig. 8. Suspension reclaiming energy power under same speed and different road grade

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(a) Class B

(b) Class C

Fig. 9. Suspension reclaiming energy power under same road grade and different speed

5 Conclusion A new hydraulic interconnected reclaiming energy suspension system is proposed by intruding a reclaiming energy unit in the traditional model of hydraulic interconnected suspension in this paper. Comparing with the traditional passive hydraulic interconnected suspension, the hydraulic motor is chosen to replace the damping valve in its structure to achieve the vibration energy collection of the suspension. Here, reclaiming energy unit is used for the suspension energy-regeneration, and the constant current control of the feed-energy unit is used to realize the adjustment and control of the vehicle dynamic performance. The relevant energy consumption model of the suspension is built and the constant current energy-regenerative circuit is designed. Finally, the corresponding bench test is carried out, the test results are basically consistent with the simulated results. It shows that the hydraulic interconnected power feedback suspension has good energy feedback foreground through the numerical analysis of the recoverable energy of the hydraulic interconnected energy suspension, which provides ideas for new type suspension design and energy recovery and utilization in vibration system. Further study on the real environment experimental implementation and engineering implementation of the designed suspension system will be conducted.

References 1. Huang C, Chen L, Jiang H et al (2014) Relationship between vehicle yaw velocity and heeling angle under steady state. Trans Chin Soc Agric Mach 45(2):34–39 2. Hawley JB (1927) Shock absorber and the like for vehicles. U.S. patent. 1647518 3. Smith WA, Zhang N, Hu W (2011) Hydraulically interconnected vehicle suspension: handling performance. Veh Syst Dyn 49(1–2):87–106 4. Cao D, Rakheja S, Su CY (2010) Roll- and pitch-plane-coupled hydro-pneumatic suspension. Part 2: dynamic response analyses. Veh Syst Dyn 48(4):507–528

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5. Guo KH, Chen YH, Zhuang Y et al (2011) Modeling and simulation study of hydropneumatic interconnected suspension system. J Hunan Univ 38(3):29–33 6. Yu CM, Wang WH, Wang QN (2010) Damping characteristic and its influence factors in energy regenerative suspension. J Jilin Univ 40(6):1482–1486 7. Kawamoto Y, Suda Y, Inoue H et al (2007) Modeling of electromagnetic damper for automobile suspension. J Syst Des Dyn 1(3):524–535 8. Asadi E, Ribeiro R, Khamesee MB, Khajepour A (2015) A new adaptive hybrid electromagnetic damper: modelling, optimization, and experiment. Smart Mater Struct 24 (7):075003

Sliding Mode Control with Uncertain Model for a Quadrotor UAV’s Automatic Visual Landing Problem Qing Fei1(&), Jiaxiang Zhang1, Zhengyang Wang1, and Xiaosong Huang2 1

Beijing Institute of Technology, Zhongguancun South Street. 5, Beijing, China [email protected] 2 The Institute of Atmospheric Physics, Chinese Academy of Sciences, Qijiahuozi Huayanli. 40, Beijing, China [email protected]

Abstract. This paper considers a quadrotor UAV’s automatic landing on unmanned vessels problem, and the quadrotor UAV’s model has uncertain parameters. We design a robust flight controller with the variable structure sliding-mode control with precise sliding manifold coefficients to ensure strong position and attitude control. The controller can move the UAV to track the desired position and yaw angle and keep pitch and yaw angles on stable. The $ Lyapunov $ function is used to prove the control system is effective, and the sliding-mode manifold coefficients are obtained by Hurwitz stability analysis. The landing simulation results show the effectiveness of the controller. Keywords: UAV
Uncertain model
Sliding-mode controller Automatic landing
Tracking control




1 Introduction Quadrotor UAV system is nonlinear system with a high degree of coupling and is susceptible to external interference [1]. A quadrotor UAV has six emerging and existing motions that are, precession motion, hover motion, roll and pitch motion, yaw motion, vertical motion and horizonal motion [2]. It is an underactuated system whose control input quantity and status output quantity are not equal. This makes the control of the quadrotor UAV more difficult. Due to the influence of nonlinearity, multi-objective and limited control, the control of underactuated system is complicated. For underactuated systems, researchers have given great attention to the control of underactuated systems and have had many valuable results [3–10]. At present, open source flight controllers often use mature PID controllers to control the attitude of the quadrotor. However, the most prominent is that their control parameters are fixed and its Robustness and adaptive ability are poor, and more often the controller only for linear systems. In response to the above problems, the neural network-based quadrotor controller [11] achieves the purpose of fast response and steady-state error by implementing coefficient self-learning and adaptive © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 226–233, 2020. https://doi.org/10.1007/978-981-32-9050-1_26

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adjustment. Attitude control based on backstepping [12] uses integral behavior to compensate for the effects of uncertainties. Liu Kaiyue et al. use the unit quaternion method to describe the system attitude, and design the inversion sliding mode controller to get good trajectory tracking effect [13], but ignored the sliding mode control chattering problem. The sliding manifold coefficients are considered to be special values in most existing UAV’s attitude and position control studies, which are assumed to be fixed values in the simulation. In reality, the UAV will often release the detection device or capture the signal device, resulting in the uncertainty of the parameters of the self-generated model. Considering the uncertainties of the aircraft model parameters, it is a challenge and significance to complete the automatic landing of the UAV. Inspired by these, this paper classifies the uncertain parameters into the sliding mode convergence coefficient and obtains the precise sliding manifold coefficients. The sliding mode controller with uncertain parameters ensures that the quadrotor maintains the attitude accurately and robustly, and perfectly tracks the landing coordinates. In Sect. 2, a quadrotor dynamics model and a pinhole camera model are given. Section 3 designs the sliding mode attitude and position controller. The simulation in Sect. 4 shows that the controller can robustly control the quadrotor to land at the target point. Finally, Sect. 5 presents the conclusions of this paper.

2 Mathematical Model The quadrotor dynamics model and the pinhole camera model are analyzed and given. The mathematical model is shown in Figs. 1 and 2.

Fig. 1. Quadrotor UAV Model

2.1

Fig. 2. Pinhole camera model

Quadrotor Dynamical Model

Figure 1 is the quadrotor configuration. We obtain the dynamic equation of the quadrotor by the Lagrangian method, and the simplified model is shown in (1).

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8 €x ¼ ðsin h cos / cos w þ sin w sin /Þu1 =m  K1 x_ =m > > > > €y ¼ ðsin h cos / sin w  cos w sin /Þu1 =m  K2 y_ =m > > < €z ¼ ðcos h cos /Þu1 =m  K3 z_ =m  g
 € ¼ h_ w_ Jy  Jz =Jx þ hJ _ r Xr =Jx þ lu2 =Jx  K4 l/=J _ x / > > > _ r Xr =Jy þ lu3 =Jy  K5 lh=J _ y > h€ ¼ /_ w_ ðJz  Jx Þ=Jy  /J > >
 :€ _ _ _ w ¼ /h Jx  Jy =Jz þ u4 =Jz  K6 w=Rz

ð1Þ

Where x; y; z denote the UAV’s gravity center position in the geodetic coordinate system; /; h; wðp=2\/\p=2; p=2\h\p=2; p\w\pÞ are the three euler angles of the UAV: roll, pitch and yaw; m is the quadrotor’s mass; g is the gravity acceleration; Jx ; Jy ; Jz is the UAV’s inertia that relative to the x; y and z axis; Jr is the propeller inertia; l is half the distance of the quadrotor from the motor; K1 ; K2 ; K3 ; K4 ; K5 ; K6 is the positive drag coefficient constant; Xr ¼ X1 þ X2  X3 þ X4 , while Xi ði ¼ 1; 2; 3; 4Þ is the angular velocity of each motor rotor. Taking the model parameter uncertainty into account, and performing the following transformation, we obtain a new model uncertainty dynamic Eq. (2). 8 €x ¼ ðsin h cos / cos w þ sin w sin /Þu1 =m  K1 x_ =m þ d1 > > > > € y ¼ ðsin h cos / sin w  cos w sin /Þu1 =m  K2 y_ =m þ d2 > > < €z ¼ ðcos h cos /Þu1 =m  K3 z_ =m  g þ d3
 € ¼ h_ w_ Jy  Jz =Jx þ hJ _ r Xr =Jx þ lu2 =Jx  K4 l/=J _ x þ d4 / > > > _ r Xr =Jy þ lu3 =Jy  K5 lh=J _ y þ d5 >  /J > h€ ¼ /_ w_ ð
Jz  Jx Þ=J >  y :€ _ _ _ w ¼ /h Jx  Jy =Jz þ u4 =Jz  K6 w=Jz þ d6

ð2Þ

^ ^Jx ; ^Jy ; ^Jz are uncertain parameter. The di ði ¼ 1; 2; 3; 4; 5; 6Þ are defined as the where m; parameter uncertainty and means: d1 d4 d5 d6

2 2 2 ^ ^ ^ ^ ^ ^ ¼ K1 x_ m=ðm þ mmÞ; d2 ¼ K2 y_ m=ðm þ mmÞ; d3 ¼ K3 z_ m=ðm þ mmÞ 2 _ _ _ _ _ _ ^ ^ ^ ^ ¼ ½hwðJy  Jz Þ þ hJr Xr  K4 l/Jx =ðJx þ Jx Jx Þ þ hwðJy  Jz Þ=ðJx þ ^ Jx Þ _ z  Jx Þ þ /J _ r Xr  K5 lh _ ^Jy =ðJ 2 þ Jy ^ _ ^ Jy Þ þ /_ wð ¼ ½/_ wðJ Jz  ^ Jx Þ=ðJy þ ^ Jy Þ y 2 _ _ _ _ _ ^ ^ ^ ^ ^ ¼ ½h/ðJx  Jy Þ  K6 w
Jz =ðJz þ Jz Jz Þ þ h/ðJx  Jy Þ=ðJz þ Jz Þ

Then u1 ; u2 ; u3 ; u4 is defined as the virtual control input, and we have: 2

3 2 1 u1 6 u2 7 6 0 6 7¼6 4 u3 5 4 1 a=b u4

1 1 0 a=b

1 0 1 a=b

32 3 1 F1 6 7 1 7 76 F2 7 0 54 F3 5 a=b F4

ð3Þ

where F1 ¼ aX21 ; F2 ¼ aX22 ; F3 ¼ aX23 ; F4 ¼ aX24 is the rotor thrust; a is the lift coefficient; b is the torque scale factor.

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Pinhole Camera Model

Pinhole imaging is a physical imaging process, creating a simple pinhole camera mathematical model as shown in Fig. 2. Where A-xyz_: ground coordinate system; C-xyz: camera coordinate system; S-rc: sensor coordinate system; F: camera focus; P: the landing point; I: imaging of the point P on the photosensitive surface; h: the distance from F to A; f: camera focal length; s: the distance between two pixels; (r, c): the coordinates of the I point; ðr0 ; c0 Þ: coordinates of point C. After the coordinate changes, we have the coordinates of the landing point relative (4) to the quadrotor ðxd ; yd ; zd Þ: Rðx; /Þ; Rðy; hÞ and Rðz; wÞ are the coordinate rotation matrix [14] and because the visual target used is rotationally symmetric, removing the third item has no effect. 2

3 2 3 ðc  c0 Þsh=f xd 4 yd 5 ¼ Rðx; /ÞRðy; hÞRðz; wÞ4 ðr  r0 Þsh=f 5 h zd

ð4Þ

3 Flight Controller Design The control problem considered in this automatic landing is to track the asymptotic position and attitude by designing a flight controller that relies on the second order sliding mode technique. Under the controller, ðx; y; zÞ ! ðxd ; yd ; zd Þ and w ! wd , that is, the quadrotor automatically landed on the target. 3.1

Surface Vessel Attitude Dynamics

Define the sliding manifold as (5): s1 s2 s3 s4

¼ ðz_ d  z_ Þ þ az ðzd  zÞ _ þ a/2 ð/  /Þ ¼ ay1 ðy_ d  y_ Þ þ ay2 ðyd  yÞ þ a/1 ð/_ d  /Þ d _ þ ah2 ðhd  hÞ ¼ ax1 ðx_ d  x_ Þ þ ax2 ðxd  xÞ þ ah1 ðh_ d  hÞ _ þ aw ð w  w Þ ¼ ðw_ d  wÞ d

ð5Þ

The first derivatives of the sliding surfaces versus time are (6): s_ 1 s_ 2 s_ 3 s_ 4

¼ ð€zd  €zÞ þ az ðz_ d  z_ Þ; €  /Þ € þ ay2 ðy_ d  y_ Þ þ a/2 ð/_  /Þ _ ¼ ay1 ð€yd  €yÞ þ a/1 ð/ d d _ ¼ ax1 ð€xd  €xÞ þ ah1 ð€hd  €hÞ þ ax2 ðx_ d  x_ Þ þ ah2 ðh_ d  hÞ €  wÞ € þ aw ðw_  wÞ _ ¼ ðw d d

ð6Þ

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Controller Design

By making s_ i ¼ gi si  ei sgnðsi Þ, the controllers for dynamic Eq. (2) are designed as (7) 8 u1 ¼ cos /mcos h ½€zd þ g þ Km3 z_ þ az ð_zd  z_ Þ þ g1 s1 þ e1 sgnðs1 Þ > > > > < u2 ¼ Jx ½ ay1 ð€yd  €yÞ þ ay2 ð_yd  y_ Þ þ ð/ €  /Þ ~ þ a/2 ð/_  /Þ _ þ g s2 þ e2 sgnðs2 Þ d d 2 l a/1 a/1 a/1 Jy ax1 ah2 _ ax2 € ~ _ > u3 ¼ l ½ah1 ð€xd  €xÞ þ ah1 ð_xd  x_ Þ þ ðhd  hÞ þ ah1 ðhd  hÞ þ g3 s3 þ e3 sgnðs3 Þ > > > : u ¼ J ½w €  /_ h_ Jx Jy þ K6 w_ þ a ðw_  wÞ _ þ g s þ e sgnðs Þ 4

z

d

Jz

Jz

w

4 4

d

4

4

ð7Þ
 _ r Xr  K5 lh, _ / ~ ¼ 1=Jy ½h_ w_ Jy  Jz þ hJ _ r Xr  Where ~h ¼ 1=Jy ½/_ w_ ðJz  Jx Þ  /J _ and the coefficients of the exponential approach laws are: e1 ¼ n1 þ jd3 j [ 0, K4 l/   e2 ¼ n2 þ ay1 d2 þ a/1 d4  [ 0, e3 ¼ n3 þ jax1 d1 þ ah1 d5 j [ 0, e4 ¼ n4 þ jd6 j [ 0. Theorem 1. Consider the quadrotor dynamics model (2) and assume that all state information is available, the design of the controller is as in (7). The system will be asymptotically stable under the control of the sliding-mode controller. Proof. Choose a Lyapunov function candidate as Vi ¼ s2i =2; ði ¼ 1; 2; 3; 4Þ, llustrate the stability analysis for subsystem ð€x; €hÞ, that is i ¼ 3. Pluging Eqs. (5) (6) into the Lyapunov function, the time derivatives of V3 is V_ 3 ¼ s3 s_ 3 ¼ g3 s23  e3 s3 sgnðs3 Þ ¼ g3 s23  ðn3 þ jax1 d1 þ ah1 d5 jÞjs3 j ¼ g3 s23  n3 js3 j  jax1 d1 þ ah1 d5 jjs3 j  0 Similarly, we have Vi  0 ði ¼ 1; 2; 3; 4Þ. Therefore, under the control of the controller (7), the system states can individually reach all the set states trajectories. 3.3

Sliding Model Coefficients

The sliding model coefficients are received under the condition of Hurwitz stability, and the following coefficients ax1 ; ax2 ; ah1 ; ah2 are solved as an example. Let s_ 3 ¼ 0 in Eq. (6) and s3 ¼ 0 in Eq. (5), then we have: _ þ ah2 ðhd  hÞ x_ d  x_ ¼  a1x1 ½ax2 ðxd  xÞ þ ah1 ðh_ d  hÞ 2 €hd  €h ¼  ax1 ð€xd  €xÞ þ ax2 ðxd  xÞ þ ðax2  ah2 Þðh_ d  hÞ _ þ ah1

ah1 ax1

ax1

ah1

ax2 ah2 ah1 ax1

ðhd  hÞ

ð8Þ

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_ X3 ¼ xd  x then Eq. (8) are: Definition X1 ¼ hd  h; X2 ¼ h_ d  h; X_ 1 ¼ X2 a2 _ þ X_ 2 ¼  aah1x1 ð€xd  €xÞ þ ah1x2ax1 ðxd  xÞ þ ðaax2x1  aah2 Þðh_ d  hÞ h1 _ þ ah2 ðhd  hÞ X_ 3 ¼  a1x1 ½ax2 ðxd  xÞ þ ah1 ðh_ d  hÞ

ax2 ah2 ah1 ax1

ðhd  hÞ

ð9Þ

After linearizing around the equilibrium point, a new form is obtained Eq. (10). X_ 1 ¼ X2

ax1 u1 K1 x_ a2 Þ þ x2 ðxd  xÞ X_ 2 ¼  ð€xd  ðX1 cos/cosw þ sin/sinwÞ þ m ah1 m ah1 ax1 ax2 ah2 _ ax2 ah2 _ ðhd  hÞ þ f1 X1 þ f2 X2 þ f3 X3 þ ð  Þðhd  hÞ þ ax1 ah1 ah1 ax1 _  ah2 ðhd  hÞ X_ 3 ¼  aax2x1 ðxd  xÞ  aah1x1 ðh_ d  hÞ ax1

ð10Þ

Written in matrix form: ½ X_ 1

X_ 2

T X_ 3  ¼ B½ X1

X2

X3 T þ C½ X1

X3 T

X2

Where 2

0 a u B ¼ 4  ah1x1 m1 cos/cosw þ  aax2x1

ax2 ah2 ah1 ax1

1 ax2 ah2 ax1  ah1 ah1  ax1

0

a2x2 ah1 ax1  aah2x1

3

2

0 5; C ¼ 4 11 0

0 12 0

3 0 13 5 0

Since 11 ; 12 ; 13 are small constants, the system is progressively stable when the real part of the matrix B eigenvalues are negatives, that is, when B is a Hurwitz matrix. Let jkI  Bj ¼ 0; k is the eigenvalues of matrix B. We set k1 ¼ 1; k2 ¼ 1; k3 ¼ 1 then the coefficients are obtained ax1 ¼

3m m ah1 ; ax2 ¼ ah1 ; ah2 ¼ 3ah1 u1 cos/cosw u1 cos/cosw

ð11Þ

Similarly, using the same method, we get ay1 ¼

3m m ah1 ; ay2 ¼ ah1 ; a/2 ¼ 3ah1 u1 cosw u1 cosw

ð12Þ

4 Simulation For the sake of verify the stability of the controller with uncertain model parameters, we tested it with the following simulations. The simulation is based on the quadrotor dynamics model of Eq. (2), using the control method of Eq. (7). This simulation proves the effectiveness of the controller in the automatic landing.

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The fixed model parameters of the UAV are as follows: m ¼ 1:1 kg; l ¼ 0:3 m; g ¼ 9:81 m=s2 ; a ¼ 5 Ns2 ; b ¼ 2 N=ms2 ; K1 ¼ 0:1 Ns=m; K2 ¼ 0:1 Ns=m; K3 ¼ 0:1 Ns=m; K4 ¼ 0:15 Ns=m; K5 ¼ 0:15 Ns=m; K6 ¼ 0:15 Ns=m; Jx ¼ Jy ¼ 1:25 Ns2 =rad; Jz ¼ 2:4 Ns2 =rad; Jr ¼ 0:2 Ns2 =rad. Jy 2 ½0:25; 0:25; Model parameter uncertainty assumptions are random: ^ Jx ¼ ^ ^Jz 2 ½0:48; 0:48; m ^ 2 ½0:2; 0:2. From Figs. 3 4, 5, 6, 7 and 8, We can clearly see that attitude and position states variables converge to their target values, and the sliding manifolds converge to their sliding surfaces, respectively. The model parameter uncertainty does not significantly affect the position and attitude tracking control results of the UAV. Therefore, the designed controller (7) can stably control the quadrotor drone to the landing point. The stability of the control method when the model parameters are uncertain is verified.

Fig. 3. Landing position

Fig. 4. Position (x, y, z)

Fig. 5. Angles

Fig. 6. Linear velocities

Fig. 7. Sliding manifolds

Fig. 8. Controllers

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5 Conclusion In this paper, we design a robust flight controller with the variable structure slidingmode control with precise sliding manifold coefficients to solve the automatic landing problem of quadrotor UAV with uncertain model parameters. The control method can stabilize the attitude of the quadrotor and track the landing target to complete the landing. To verify the controller, a simulation was performed. The results of this study are as follows: UAV’s attitude and position variables can converge to the target value; each sliding variables can converge to their sliding surfaces; the influence of model parameter uncertainty on the tracking control is invisible, proving the stability of the designed controller. In summary, the vision-based UAV automatic landing slidingmode controller is reasonable and effective.

References 1. Wang H, Chen M (2014) Sliding mode attitude control for a quadrotor micro unmanned aircraft vehicle using disturbance observer. In: Guidance, navigation and control conference. IEEE (2014) 2. Ashrafiuon H, Erwin RS (2008) Sliding mode control of underactuated multibody systems and its application to shape change control. Int J Control 81(12):1849–1858 3. Islam S, Liu PX, El Saddik, A (2015) Robust control of four-rotor unmanned aerial vehicle with disturbance uncertainty. IEEE Trans Ind Electron 62(3):1563–1571 4. Xiong JJ, Zheng EH (2014) Position and attitude tracking control for a quadrotor UAV. ISA Trans 53(3):725–731 5. Xiong JJ, Zheng EH (2015) Optimal kalman filter for state estimation of a quadrotor UAV. Optik – Int J Light Electron Optics 126(21):2862–2868 6. Kurode S, Bandyopadhyaya B, Gandhi PS (2011) Discrete sliding mode control for a class of underactuated systems 7. Cozaa C, Nicola C, Macnaba CJB, Ramirez-Serranob A.: Adaptive fuzzy control for a quadrotor helicopter robust to wind buffeting. J Intell Fuzzy Syst 22(5):267–283 8. Mondal S, Mahanta C (2012) A fast converging robust controller using adaptive second order sliding mode. ISA Trans 51(6):713–721 9. Guo ZQ, Xu JX, Lee TH (2014) Design and implementation of a new sliding mode controller on an underactuated wheeled inverted pendulum. J Franklin Inst 351(4):2261– 2282 10. Zhang X, Xian B, Zhao B, Zhang Y (2015) Autonomous flight control of a nano quadrotor helicopter in a GPS-denied environment using on-board vision. IEEE Trans Ind Electron 62 (10):6392–6403 11. Xu G, Zhou M (2013) Modified adaptive flight control of quadrotor based on single neuron PID. In: IEEE third international conference on information science and technology. IEEE 12. Tan L, Lu L, Jin G (2013) Attitude stabilization control of a quadrotor helicopter using integral backstepping. In: International conference on automatic control and artificial intelligence, IET 13. Kai-Yue L, Jian-Wei L (2017) Trajectory tracking control of quadrotor UAV based on sliding mode control. J Tianjin Univ Technol 14. Zheng EH, Xiong JJ, Luo JL (2014) Second order sliding mode control for a quadrotor UAV. ISA Trans 53(4):1350–1356

Depth-Fusion Based on Gaussian Mixture Model for RGB-D Visual SLAM Zhaotong Ding, Ran Huang(&), and Biao Hu College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China {huangran,hubiao}@mail.buct.edu.cn

Abstract. Stochastic noises and disturbances in depth image about a scene observed by a RGB-D camera degrade the performance of a visual simultaneous localization and mapping (SLAM) system. For enhancing perception robustness, we develop a real-time SLAM system which uses a RGB-D camera as its sole sensor modality. The depth uncertainty for RGB-D features is described by Gaussian mixture model (GMM). A frame-constrained depth-fusion approach is then proposed to obtain accurate depth information of the current frame using the past frames in a local window. Experiments performed on public RGB-D TUM dataset have showed the proposed system outperforms the ORB-SLAM2. Keywords: Depth fusion


Gaussian mixture model
Visual SLAM

1 Introduction The motivation for the study of autonomous localization and navigation has its roots in industry since mobile robots must have the ability of mapping, localization, path planning and obstacle avoidance in a complex environment [1]. These competencies can be achieved using vision sensors that can offer more information of a scene in comparison with laser range finder. Therefore, visual simultaneous localization and mapping (V-SLAM) plays an important role in the computer vision community; see [2], and the references therein. In the early stage of the research, V-SLAM was mainly performed by a monocular camera [3]. This type of SLAM may result in depth scale uncertainty and incorrect state initialization due to unmeasurable depth information. Practical demands of robust and precise perception lead researchers to approach VSLAM systems by utilizing more complicated vision sensors [4]. Among them RGB-D cameras have been widely used in recent years. However, as illustrated in Fig. 1, there always exist black holes in each depth image detected by Kinect v1. The reason mainly lies in the fact that uncertainty of sparse features exists in the depth measurement, and depth measurements of detected features may be lost because of disturbances, the range limitation of sensors. As a result, the camera trajectory may be estimated incorrectly. Therefore, the problem of designing depth-fusion algorithm for V-SLAM with uncertainty to achieve robust perception has not been fully explored, which motivates the present study.

© Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 234–242, 2020. https://doi.org/10.1007/978-981-32-9050-1_27

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Fig. 1. Images captured by the Kinect v1

In this paper, a depth-fusion V-SLAM system is presented to achieve robust perception in the environment. Firstly, a method based on the GMM is developed to estimate depth uncertainty of the RGB-D camera. Concretely, we suppose that the depth uncertainty depends on the depth reading of a given pixel as well as those of its neighbors. It has been shown by experiments that the GMM describes the uncertainty better than the simple model. Secondly, in order to recover the depth information affected by external noises, a frame-constrained depth fusion method is proposed using the coordinate information of the past frames in a local window. The fused depth image is then used to estimate trajectory by performing alignment and data association against a map of 3D features. The rest of this paper is organized as follows. In Sect. 2, we discuss related work. Section 3 details the V-SLAM with depth-fusion based on GMM. Experiments performed on RGB-D TUM dataset are performed in Sect. 4. Finally, we conclude in Sect. 5.

2 Related Work As an effective sensor for providing the color and depth information, RGB-D SLAM system has been a hot research topic in recent years. A fusion system was developed for 3D scene reconstruction using a RGB-D camera in [5]. In this system, the iterative closest point (ICP) algorithm was employed to perform pose estimation with the point cloud data, and the reconstruction result was expressed by the truncated signed distance function model. Nevertheless, this system can only be applied to small-scale environment because loop closure is not included. In [6], a SLAM system was developed to align image based on a composite optimization method, using both sparse features and shape-based information, but this system needs a GPU to accelerate. In addition, a fast visual odometry and mapping system was developed in [7], where sparse features are registered against a lasting model, which has a limited size, then the above model is updated through Kalman Filter. The use of Kalman Filter limits the system for long mission as landmarks increase. Based on a RGB-D camera, a new 3D mapping system using SIFT descriptors was developed in [8]. In this system, the front-end uses ICP to estimate camera pose between two frames, while the back-end executes pose-graph optimization. Unlike the above methods, we use bundle adjustment as back-end to optimize camera pose and landmarks simultaneously.

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3 Main Results In this paper, a RGB-D visual SLAM system relying on local window techniques is developed. The system pipeline is briefly depicted in Fig. 2. Firstly, for performing the alignment and data association accurately, a Gaussian mixture model is introduced to denote depth uncertainty. We use the perspective-n-point algorithm to perform pose estimation. The Levenberg-Marquardt optimization method is then used to solve the nonlinear least square problem. Meanwhile, the transformation information of the past frames in a local window is fused to estimate the missing depth measurement of a pixel in the current frame due to external disturbance and noise. 3.1

Model Uncertainty

The proposed V-SLAM system uses a Microsoft Kinect v1 as the RGB-D camera. 2 3 Denote the camera inverse projection by p1 m ð
Þ : R  R ! R . Given its raw depth measurement d and a pixel p ¼ ðu; vÞ, we have a corresponding point q ¼ ½x; y; zT in the camera coordinate that q¼

p1 m ðp; d Þ


 T d ð u  cx Þ d u  cy ¼ ; ;d fx fy 

ð1Þ



 where fx ; fy denotes focal length, cx ; cy is the optical center. In [9], the depth measurement d is viewed as a random variable with standard deviation rd . We consider a more general model in this paper. It is assumed that the uncertainty in u and v dimensions exists, and the depth uncertainty depends on the depth reading of a given pixel as well as those of its neighbors in
a local  window.
Let u,  v, z be normally distributed random variables satisfying u  N lu ; r2u , v  N lv ; r2v ,
   and d  N ld ; r2d . In addition, define a Guassian kernel W ¼ wij 2 R33 .

Fig. 2. The flowchart of the proposed V-SLAM

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For each pixel p, define a new random variable d^ satisfying d^ 

X

 2 w N l ; r dij dij i;j ij

ð2Þ

 where N ldij ; r2dij is the normal distribution of the ij th pixel in the local window. In other words, the expectation and variance of the estimated depth of pixel p are given by X ^d^ ¼ w l ; ð3Þ l i;j ij dij ^2d^ ¼ r

X i;j

^2d^ wij ðl2dij þ r2dij Þ  l

ð4Þ

We observe that the GMM of the following Guassian kernel predicts the true uncertainty more accurately than the simple model [9] 2 1 2 1 4 2 4 K¼ 16 1 2

3 1 25 1

ð5Þ

through various comparison experiments. Using Eqs. (1) and (4)–(5), we obtain the GMM parameters for q in the camera coordinate corresponding to each given pixel p. 3.2

Frame-Constrained Depth Fusion

It can be clearly seen from Fig. 3 that there exist some black holes in the depth image with GMM, which means that depth information of certain pixels is still missing due to noises. To recover the missing depth information, a frame constrained depth fusion algorithm is proposed by taking the depth information of the past frames in a local window into consideration.

(a) f2/xyz original depth image

(b) f2/xyz depth image with GMM

Fig. 3. The original depth image derived from GMM.

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Firstly, we use a vector f to denote the variables to be optimized, which includes both the seð3Þ pose of each keyframe ngi and 3D position of each point Qgj in the world 3 2 coordinate.
Define the camera projection function by pm : seð3Þ  R ! R , and pm ngi ; Qgj stands for mapping the j th 3D point Qgj to the local reference system of the i th keyframe, i.e. Qij , and then projecting Qij to the 2D image. In addition, we introduce a cost function X X J ð fÞ ¼ eTij C1 ð6Þ eij eij i2K j2P m

m

where Km and P m refer to the sets of local keyframes and points respectively, eij are the projection errors between the observations and the landmarks mapped to the frames where they are observed, i.e.,
 eij ¼ pij  pm ngi ; Qgj

ð7Þ

and Ceij denotes the covariance matrix that is corresponded to the uncertainty of the project error eij . The camera pose is optimized to minimize the cost function (6), i.e., f ¼ arg minf J ðfÞ:

ð8Þ

In the sequel, we adopt the Levenberg-Marquardt optimization approach to solve the problem (8). The increment Δf is given by Mf ¼ ½H þ kdiagðHÞ1 J T We

ð9Þ

where e is a vector encompassing all reprojection errors eij , and W is a residual weight matrix with compatible dimensions, J and H are the Jacobian and Hessian matrices with respect to f. According to (7), the projection error eij depends on a single keyframe ngi and a single point Qgj , hence the Hessian matrix can be given by 2 6 .. 6. 6 6 H6 6 6. 4 ..

...

@eij T @ngi

... ...

@eij T @Qgj

...

@e

ij C1 eij @n

gi

@e

ij C1 eij @n

gi

...

.. .. . .

@eij T @ngi

.. .. . .

@eij T @Qgj

... ... ...

@e

ij C1 eij @Qgj

@e

ij C1 eij @Qgj

3 .. 7 .7 7 7 7 7 .. 7 .5

ð10Þ

The iterative equation for f is given by ~f ¼ Mff

ð11Þ

where the operator  stands for a left multiplication for Lie-algebra seð3Þ elements. Applying (11) recursively until convergence yields the optimal f , which will be used

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to update the position of the local keyframes and points. It should be noted that certain transformation will be made according to the nature of the variables in f before h iT applying the update Eq. (11). For a given ngi ¼ xTgi vTgi 2 seð3Þ, the exponential map is used to project the vector ngi to the Lie group SEð3Þ, i.e.,
 exp ngi ¼ exp

 

xgi 0

 

vgi 1

 ð12Þ

  with xgi  being the skew symmetric matrix generated by the cross product of the vector xgi .

Fig. 4. A set of points with their uncertainty projected to the current frame.

As depicted in Fig. 4, 3D points are projected to the current image using the optimal solution f , the relative pose matrices and 3D measurements of past frames in a local window. As a result, we can estimate the depth with uncertainty of each pixel in the current image by using the depth information of projected points. Concretely, the fusion depth of each pixel d^ is given by Xn

1 d^ ¼ Pn

1 i¼1 r ^2d

i¼1 i

di ^2di r

ð13Þ

where n denotes the number of projected points in the image grid of current frame, and ^d^i is the mixed Gaussian variance defined in (4). Finally, we obtain the depth comr pensation for the missing pixels, which will be used in the trajectory estimation phase.

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4 Experiments This section validates the usefulness of our V-SLAM system in several scenarios from TUM RGB-D dataset. The comparison between our method and the RGBD version of ORB-SLAM2 by employing its open source implementation is also presented. For brevity, we consider five sequences in the experiment. Note that the absolute trajectory error (ATE) is used to evaluate the performance. In what follows, we present the experiment results performed on TUM RGB-D dataset. We can see that the GMM predicts the true uncertainty more accurately than a simple model from Fig. 5(b) and (e). Besides, Fig. 5(c) and (f) show that the missing depth measurements of the raw depth images are successfully recovered by our proposed depth fusion algorithm. We evaluate our trajectory estimation pipeline with the two filtered sequences f1/xyz and f1/desk2.

(a) f2/xyz original depth

(b) f2/xyz depth (GMM)

(c) f2/xyz depth (fusion)

(d) f1/sitting original depth

(e) f1/sitting depth (GMM)

(f) f1/sitting depth (fusion)

Fig. 5. Analysis for the proposed depth fusion algorithm. Left column: the original depth image. Middle column: the depth image using GMM. Right column: the depth image using frameconstrained depth fusion.

Scenario 1: sequence f1/xyz. The groundtruth, estimated camera trajectories derived from RGB-D ORB-SLAM2 [10] and the proposed V-SLAM are illustrated in Fig. 6(a) and (b), where the trajectory errors are visualized with different color. Figure 6(c) shows that the estimated trajectory of the proposed method is closer to the groundtruth than that of ORB-SLAM2. Scenario 2: sequence f1/desk2. Similarly, the groundtruth, estimated camera trajectories, and ATE errors derived from RGB-D ORB-SLAM2 and the proposed V-SLAM are depicted in Fig. 6(d), (e) and (f) respectively, which implies the proposed approach outperforms the ORB-SLAM2.

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Scenario 3: sequence f1/xyz, f1/desk and f1/desk2. In this scenario, each sequence is simulated for 5 times. The experiment results in Table 1 shows that the proposed method achieves better estimation than ORB-SLAM2 in all test sequences. Our method using depth-fusion framework decreases the trajectory error.

(a) RGB-D ORB-SLAM2

(b) The proposed method

(c) The ATE comparison

(d) RGB-D ORB-SLAM2

(e) The proposed method

(f) The ATE comparison

Fig. 6. The comparison results performed on the sequence f1/xyz and f1/desk2. Table 1. The RMSE of ATE of the keyframe trajectory Sequence ORB-SLAM2 RMSE RMSE f1/xyz 0.0112 0.0108 f1/desk 0.0177 0.0164 f1/desk2 0.0262 0.0257

Our method Median STD 0.0084 0.0053 0.0112 0.0080 0.0214 0.0115

5 Conclusion A robust real-time RGB-D SLAM system has been proposed in this paper. Firstly, we introduced a Gaussian mixture model (GMM) to describe the depth model uncertainty. We have shown the GMM predicts the true uncertainty more accurately than a simple Gaussian model by experiments performed on static scenes. In order to obtain the missing depth information of the pixels in current frame, a frame-constrained depthfusion approach has been developed using the past frames in a local window. The proposed V-SLAM has been tested on public TUM RGB-D dataset. Our experimental results have showed the proposed SLAM system outperforms the ORB-SLAM2 system.

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Ackownagement. This work was supported in part by National Natural Science Foundation of China under Grants 91748102 and U1813220, by the Fundamental Research Funds for the Central Universities XK1802-4 and Research projects on biomedical transformation of ChinaJapan Friendship Hospital PYBZ1837, and by the CSC scholarship.

References 1. Nistér D, Naroditsky O, Bergen J (2006) Visual odometry for ground vehicle applications. J Field Robot 23(1):3–20 2. Cadena C, Carlone L, Carrillo H, Latif Y, Scaramuzza D, Neira J, Reid I, Leonard JJ (2016) Past, present, and future of simultaneous localization and mapping: toward the robustperception age. IEEE Trans Robot 32(6):1309–1332 3. Davison AJ, Reid ID, Molton ND, Stasse O (2007) MonoSLAM: real-time single camera slam. IEEE Trans Pattern Anal Mach Intell 6:1052–1067 4. Gallego G, Lund J, Mueggler E, Rebecq H, Delbruck T, Scaramuzza D (2018) Event-based, 6-DOF camera tracking from photometric depth maps. IEEE Trans Pattern Anal Mach Intell 40(10):2402–2412 5. Newcombe RA, Izadi S, Hilliges O, Molyneaux D, Kim D, Davison AJ, Kohi P, Shotton J, Hodges S, Fitzgibbon A (2011) Kinectfusion: real-time dense surface mapping and tracking. In: 2011 IEEE international symposium on mixed and augmented reality, pp. 127–136 6. Henry P, Krainin M, Herbst E, Ren XF, Fox D (2012) RGB-D mapping: using kinect-style depth cameras for dense 3D modeling of indoor environments. Int J Robot Res 31(5):647– 663 7. Dryanovski I, Valenti RG, Xiao JZ (2013) Fast visual odometry and mapping from RGB-D data. In: 2013 IEEE international conference on robotics and automation, pp. 2305–2310 8. Endres F, Hess J, Sturm J, Cremers D, Burgard W (2014) 3-D mapping with an RGB-D camera. IEEE Trans Robot 30(1):177–187 9. Khoshelham K, Elberink SO (2012) Accuracy and resolution of kinect depth data for indoor mapping applications. Sensors 12(2):1437–1454 10. Mur-Artal R, Tardós JD (2017) ORB-SLAM2: an open-source slam system for monocular, stereo, and RGB-D cameras. IEEE Trans Robot 33(5):1255–1262

Error Analysis of Dual Antenna UAV Tracking System Shujuan Li1(&), Junhang Ding2, and Jianzhi Li3 1

2

Shandong Institute of Commerce and Technology, 4516 tourist road, Jinan, China [email protected] Qingdao University School of Automation, 308 Ningxia road, Qingdao, China 3 Shandong Quanqing Communication Co. Ltd., Jinan, China

Abstract. Aiming at the tracking problem of directional antenna in uav flight detection, this paper studies the principle of using dual antenna for target tracking, and analyzes the tracking system theoretically. In order to ensure the accuracy and reliability of data sending and receiving, and improve the receiving gain and anti-interference ability, the error of uav flying in different longitude and latitude was analyzed, and the relationship between distance d and pitch angle was obtained through simulation results. In order to reflect the tracking effect of the antenna tracking platform in the uav transition field and keep the sampling frequency unchanged, single antenna and double antenna tracking modes were adopted to track, observe the changes of RSSI and further determine that the dual antenna system has advantages of strong capture sensitivity and fast tracking speed compared with the single antenna system. Keywords: Dual antenna


Tracking
Pitch angle
Error

1 Introduction Please note that the first paragraph of a section There is a large amount of data to be transmitted between the uav and the ground control system. Automatic tracking, which is the main tracking method at present, is a directional antenna that automatically realizes the target tracking according to the target movement. The research on geographic position tracking mainly includes: automatic tracking by data fusion based on the extraction of geographical position information on uav [1], asynchronous dualmode fusion using motion compensation prediction algorithm and channel gain feedback alignment algorithm [2], and single antenna automatic tracking [3]. When GPS services are turned off or disabled in some areas, the tracking quality will be seriously affected. Asynchronous fusion dual mode can increase the computing complexity and computing time, and Single antenna tracking has the disadvantages of slow tracking speed and difficulty in acquisition [4], found in the development process, determined according to the relative position of the unmanned aerial vehicle (uav) and directional antenna tracking target angle [5], there are a variety of methods, but sometimes in order to reduce the computational complexity and simplified [7], some must judge the true

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azimuth in quadrant, increased the complexity of the control system. Based on the above reasons, this paper proposes to adopt dual antenna tracking to solve the above problems.

2 Principle of Dual Antenna Position Tracking The ground end obtains the position information of the uav through the GPS system, takes the extracted signal reception strength of the ground end antenna as a reference, adjusts the antenna direction by using the step tracking algorithm, and realizes the dynamic tracking of the uav [6]. In the dual antenna tracking, the next rotation direction of the antenna can be determined by comparing the range of the main beam of the two antennas, so as to improve the tracking speed [8]. The details is shown in Fig. 1.

Fig. 1. Dual antenna pattern.

3 Theoretical Analysis of Position Tracking The source of position tracking signals can be divided into two types. This system mainly obtains the geographical position information of the target through the interaction between the antenna and the target information, and calculates the position angle based on its own geographical position information, so as to obtain the benchmark tracking target of the whole system [9]. The antenna and the target’s longitude, latitude and height can be obtained through GPS/beidou signal. When the antenna gets the target’s position information, the antenna calculates the pitch, horizontal and roll angle of the two communications through software, and the variation deviation angle of the horizontal, pitch and roll can be obtained by doing the difference. This deviation angle is the input of the tracking system. When the system adopts a two-axis tracking strategy, the deviation value is simulated under ideal conditions [10]. There are two major deviations in the use of geographic location information tracking. One is the deviation caused by the transmission of beacon data interval, and the other is the deviation caused by the accuracy of geographic location information. The following are the theoretical analysis of the two deviations.

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Fig. 2. Aerial coverage of the hovering area of the uav.

As shown in Fig. 2, it is assumed that the uav moves in a circle over the sky at a speed of v = 60 km/h. The height of the uav is h = 3000 m, the flight radius is R = 1000 m, and the horizontal distance between the center of the circle and the observation point is d. In this case, the relationship between the elevation angle difference and the horizontal angle difference of the observation point relative to the uav position within the sampling time T and the horizontal distance d is calculated. In the case of fixed d, the difference in pitch angle and horizontal angle of observation points generated by the flight of uav in T time are also different. Therefore, the position of the center of the circle is firstly fixed and each point of the entire circumference is taken as the starting point to calculate the difference in pitch angle and horizontal angle of observation points generated in T.

Fig. 3. Horizontal and vertical projection of uav trajectory.

The vertical projection and horizontal projection of uav flight track are shown in Fig. 3. When the starting point of uav is A and it is located at B after time T, the

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elevation angle difference of observation point generated by uav flight arc AB is calculated. The elevation angle difference is as shown in the figure, and the horizontal angle difference is illustrated as the heroine. Since the speed of the uav is known, the length of arc AB can be calculated to get the angle. b¼

# 0:1   360 3:6 2  p  R

ð1Þ

Pitching angle c tan h ¼

H d þ R  cos a

H d þ R  cos ða þ bÞ

 H H c ¼ tan 1  tan 1 d þ R  cos ða þ bÞ d þ R  cos a tan ðh þ cÞ ¼

ð2Þ ð3Þ ð4Þ

Make angle change from 0 to 360°. According to the above formula, the pitching angle difference of each observation point obtained by uav at the beginning of each position can be obtained. Horizontal angle s tan r ¼ R 

sin a d þ R  cos a

sin ða þ bÞ d þ R  cos ða þ bÞ

 sin ða þ bÞ sin a s ¼ tan 1 R   tan 1 R  d þ R  cos ða þ bÞ d þ R  cos a tan ðr þ sÞ ¼ R 

ð5Þ ð6Þ ð7Þ

Also, the angle of is changed from 0 to 360°. According to the above formula, the horizontal angle difference of each observation point obtained by the uav at the beginning of each position can be obtained. Space close angle u u ¼ cos

1

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi! 1 ð8Þ 1 þ tan ðabsðsÞÞ  tan ðabsðsÞÞ þ tan ðabsðcÞÞ  tan ðabsðcÞÞ

The maximum deviation angle within the change range of each fixed d distance uav is taken as the maximum deviation angle at this moment, and d is taken as the change range from 0 to 100 km. The simulation results are shown in Fig. 4.

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0.1

0.08

0.06

100ms

0.04

0.02

0

-0.02

-0.04 0

50

100

150

200

250

300

350

400

Fig. 4. The relationship between the variation of the triaxial deviation angle at a fixed d and the flight circle of uav.

According to the experimental data, it can be concluded that the larger d is, the smaller the angle range the antenna needs to rotate, and the smaller the effect of the hovering radius R on the pitch angle of the uav is.

4 The Error Analysis Since there is an error between the GPS signal of the system and the target, namely, there is an error between the longitude and latitude, and the variation of the error between the longitude and latitude is shown in Figs. 5 and 6. -5

x 10 5

0

-5 0

2000

4000

6000

8000

Fig. 5. Longitude deviation. -5

x 10 4 2 0 -2 -4 0

2000

4000

6000

8000

Fig. 6. Latitude deviation.

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The earth’s radius is about 6371.393 km, and the equatorial circumference is about 40032642 m. The longitude of earth is divided into east and west longitude 180°, which adds up to 360°. By the graphics shown in longitude error is not more than 7 * 10 ^ (−5)°, so determined by longitude location deviation of 40032642/360 * 7 * 10 ^ (−5) m, which is about 7.7 m. The latitudes of earth are 90° north and 90° south, which adds up to 180°. By the graphics shown in latitude error is not more than 4 * 10 ^ (−5)°. So determined by latitude location deviation of 40032642/180 * 4 * 10 ^ (−5) m, which is about 8.9 m. In conclusion, taking 10 m as the deviation range of GPS positioning, the influence of this deviation in the calculation process of pitch angle and horizontal angle was calculated. Take the system as the origin and the radius of 10 m as the error calculation range. The error radius of the target uav is also 10 m, as shown in Fig. 7.

Fig. 7. Calculation diagram of horizontal angle of deviation pitching.

According to the figure, ∠1 is the standard pitch angle and ∠2 is the deviation pitch angle; Standard level angle for 0°, ∠4 levels for deviation angle. The calculation formula is as follows: \1 ¼ tan 1

H d

ð9Þ

H \2 ¼ tan 1 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð10Þ ðd  10  cos ð\3Þ þ 10  cos ð\5ÞÞ2 þ ð10  sin ð\5Þ  10  sin ð\3ÞÞ2

\4 ¼ tan 1

10  sin ð\5Þ  10  sin \3 d  10  cos ð\3Þ þ 10  cos ð\5Þ

ð11Þ

Error Analysis of Dual Antenna UAV Tracking System

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10 1.4

1.2

1

0.8

0.6 X: 3003 Y: 0.3812

0.4

X: 1.001e+04 Y: 0.1144

0.2 X: 5005 Y: 0.2287

X: 2.002e+04 Y: 0.05718

X: 3.003e+04 Y: 0.03812

0 0

0.5

1

1.5

2

2.5 d(m)

3

3.5

4

4.5

5 4 x 10

Fig. 8. Maximum horizontal deviation angle of 10 m circle. 10 0.4 0.35 0.3 0.25 0.2 0.15 0.1

X: 5005 Y: 0.101 X: 1.001e+04 Y: 0.03147

0.05 0 -0.05 0

1

2

3

4

5 d(m)

6

7

8

9

10 4 x 10

Fig. 9. Maximum angular deviation of a 10-m circle (pitch).

The calculation results are as follows: According to the result of the calculation of Figs. 8 and 9, you can see that when the unmanned aerial vehicle (uav) and tracking system location deviation within 10 m, more than 5 km, and the distance between horizontal deflection angle is not higher than 0.2287°, pitch deviation angle is not higher than 0.101°, tracking error in the system under scope. In order to reflect the tracking effect of the antenna tracking platform in the transition field of uav, the sampling frequency was kept unchanged, and the single antenna and double antenna tracking modes were used for tracking respectively. The changes of RSSI were observed and recorded in Fig. 10.

Fig. 10. Comparison chart of test results of uav transition tracking.

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5 Conclusion Based on the above theory and the simulation results, when tracking the air target, the horizontal position tracking error is not higher than 0.2374° within 5 km, and not higher than 0.1250° within 10 km. The pitching direction position tracking error is not higher than 0.1088° within 5 km, and not higher than 0.0341° within 10 km, due to large communication allowance when 5 km, 0.3° deviation will not affect communication effect. As the distance increases, the position error becomes smaller and smaller, and the tracking error is within the bearing range of the system. It was found that dual-antenna tracking can obviously reduce the pitch angle deviation, increase the main beam width of the antenna, and improve the capture sensitivity, tracking speed and tracking accuracy.

References 1. Wada A, Yamashita T, Maruyama M (2015) A surveillance system using small unmanned aerial vehicle (UAV) related technologies. J NEC Tech J 8(1):68–72 2. Jing H, Wen X, Jun L (2015) Directional antenna alignment of relay communication UAV based on mode fusion. J Comput Simul 32(8):225–229 (in Chinese) 3. Dehghan S, Moradi H (2014) A new approach for simuhaneons localization of UAV and RF sources (SLUS). In: International conference on unmanned aircraft systems, NJ. IEEE Press, pp 744–749 4. Fan J, Gao X, Ding J (2014) Research of stable tracking system for the directional antenna of UAV. J Sichuan Ordnance 34(4):84–85 (in Chinese) 5. Du M, Chai L (2008) The design of antenna track on GPS. J Microcomput Inf. 24(25):243– 244 (in Chinese) 6. Yang MK, Gao GP, Zhang TL (2013) Design of automatic tracking system for sonde application. J Commun Technol 45(7):1–3 (in Chinese) 7. Zhang ZQ, Li XB (2013) Research on antenna automatic control of dynamic point—to— point communication. J Modem Defense Technol, 41(4):94–99 (in Chinese) 8. Gohiya CS, Sadistap SS, Akbar SA (2013) Design and development of digital PID controller for DC motor drive system using embedded platform for mobile robot. In: IEEE International Advance Computing Conference (IACC), NJ, IEEE Press, pp 52–55 9. Lin Y, Yu Q (2011) Efficient detection and tracking of moving objects in geo-coordinates. J Mach Vis Appl 22(3):505–520 (in Chinese) 10. Yang ZG (2013) Development of active directional antennae for use in small UAVs. Innovation, Communication and Engineering, pp 169–172 (in Chinese)

Fault Tolerant Control Allocation Based on Adaptive Sliding Mode Control for Distributed-Driven Electric Vehicle Guohai Liu, Shuangjian Wang(&), Duo Zhang, Yue Shen, and Zhen Yao School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China [email protected]

Abstract. Distributed-Driven Electric Vehicle (DDEV) faces many challenges in the four hub motors coordination control and vehicle handling stability. The probability of failure is greatly increased because of the redundancy of the DDEV actuator. When the actuator fails, the driver may be misled by the undesired vehicle performance which makes the vehicle lose the stability under critical condition. In this paper, sliding mode control strategy with fault-tolerantcontrol-allocation (FTCA) is adopted to improve the performance of the DDEV under extreme conditions with actuator failure. In the control layer, the sideslip angle is obtained by the state observer (SMO). In order to improve the robustness, an adaptive sliding mode control (ASMC) is used. Then, the FTCA is designed to distribute the torques of the four hub motors properly when the motor fails. The proposed ASMC-FTCA algorithm was verified through some simulation combined with Carsim and MATLAB. Keywords: Distributed driven electric vehicle (DDEV)
State observer Adaptive sliding mode (ASM)
Fault tolerant control allocation (FTCA)




1 Introduction Distributed-Driven Electric Vehicle(DDEV) have great potential for driving flexibility, efficiency, and performance. The trajectory tracking ability can be improved [1, 2]. However, with the increasing of the actuator number, the possibility of the actuator failure increases. It is hard to control vehicle when the hub motor is broken especially on the slippery road. In order to estimate the fault of the system, several fault diagnosis strategies have been proposed in [3, 4]. The researches of the FTC for DDEV mainly focused on the failure of the driving motor. In [5] an adaptive algorithm was established to keep the safety of DDEV system when fault happen. In [6], a FTC controller was designed based on the robust gain-scheduling algorithm. In [7], the tracking control problem for the DDEV with actuator fault and unmatched disturbance was investigated. Literature [8] and [9] used the idea of combining Linear Quadratic and Lyapunov functions to solve the path tracking problem when motor fails. © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 251–261, 2020. https://doi.org/10.1007/978-981-32-9050-1_29

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In order to ensure the maneuverability and the reliability of the DDEV, there have been many advanced control methods. In [10], a proportional-integral-derivative (PID) controller combined with neural network was proposed for improving the maneuverability of DDEV. In [11], the direct yaw-moment control (DYC) strategies were applied to DDEV in critical condition which combined sliding mode control and nonlinear disturbance observer (NDOB). [12] proposed nonlinear torque allocation strategy combined with model predictive control to enhance the yaw stability of DDEV. The purpose of the yaw stability control of the vehicle is to regulate the yaw rate and sideslip angle stay close with the reference value. The state observer is used to obtain the sideslip angle. To improve the robustness, the additional yaw moment of DDEV is obtained by the ASMC. In CA layer, the character of control signal is taken into consideration. Meanwhile, the outputs of the distributed driven system will be reassigned to keep the vehicle safety after fault happens. The paper is organized as follows. In the Sect. 2, vehicle dynamic model of the DDEV is established. In the Sect. 3, based on the fault tolerant control allocation (FTCA), the ASM controller with a new adaptive exponential approach law is designed. And, the sideslip angle SMO is also given. The simulation results of the DDEV in two faulty cases are given in Sect. 4. Section 5 gives conclusions.

2 DDEV with Fault Model 2.1

Faulty Factor of the Drive Motor

For simplification, the combination of motor controller and motor is assumed as the actuator of the DDEV. Therefore, the actuator control signal u and the torque output T is simply described as a control gain k. ki ¼

Ti ; u  u 
u ui

ð1Þ

u and
u are the top and bottom output limits of the control signals. i 2 {fl, fr, rl, rr}, it means four driving motor. If the actuator fails, the output torque will be different to its ideal value. It assumed that the control signal is changed. So the control gain can be expressed as ^ki ¼ ð1  qi Þki

ð2Þ

The faulty factors qi is subject to 0–1 which describes the degree of actuator fault. i 2 {fl, fr, rl, rr}, it means four driving motor. 2.2

DDEV Dynamic Model with Motor Fault

Figure 1 is the schematic of DDEV model. The kinetic functions of the motion of the DDEV are described as

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253

Fig. 1. Schematic of DDEV model.

8 > >
> 2C r Cr Þ : b_ ¼ 2ðCf þ Cr Þ b þ 2ðlf Cf l  1 c þ f df 2 mVx

mVx

ð3Þ

mVx

Where Vx and c are vehicle velocity and yaw rate. m is the DDEV mass. df and b represent the front steering angle and sideslip angle. lf and lr are the distances from center of gravity to front and rear suspension. The rotational inertia of the vehicle is represented by Iz. The longitudinal force Fx and additional yaw moment Mz generated by the four longitudinal tire force Fxi is described as 

Fx ¼ Fxfl þ Fxfr þ Fxrl þ Fxrr  Mz ¼  d2 Fxfl  Fxfr þ Fxrl  Fxrr

ð4Þ

the DDEV dynamic model can be established as  " Beq ¼

^kfl mR d^k  2Iz flR

^kfr mR d^kfr 2Iz R

V_ x c_



 ¼

^krl mR ^  2IdkzrlR

 f1 ðxÞ þ Beq ½ ufl f2 ðxÞ ^krr mR d^krr 2Iz R

# ( ;

f2 ðxÞ ¼

ufr

url

urr T

f1 ðxÞ ¼ bvx c 2Cf l2f þ 2Cr l2r 2Cf lf 2Cr lr bþ Iz I z vx

ð5Þ

c

2Cf lf Iz

df

3 Control System Design Figure 2 shows the control framework. The reference yaw rate cref and velocity Vxr are acquired based on the df and pedal signal. In the upper controller, the additional yaw moment Mz is produced by ASMC. Longitudinal force Fx is obtained by proportionalintegral (PI) controller. Then, the FTCA distributes torque to the four motors based on these two virtual control inputs and the fault factor.

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ASM-FTCA

β

+ -

γ ref +

Pedal signal Reference Model V + δf ref

Vx

βˆ

ASM

FT-CA

-

γ

-

δf

MZ

PI

up

4WID Drive syetem

Tx

FX

Fault Diagnosis ρ

FZ

Sideslip Angle Observer

γ

Fig. 2. Framework of the ASMC-FTCA.

3.1

Sideslip Angle Observer

In this paper, c and the lateral acceleration ay are considered as the input variable of the state observer. When vehicle speed Vx is invariant the observer is designed as 8   1 _ ^ > ay < ^c ¼ a22 c þ a21 b þ b2 df þ q1 j~cj2 signð~cÞ þ q3 ~   1 _ ^ ^ 2 b ¼ a12 c þ a11 b þ b1 df þ q2 j~cj signð~cÞ þ q4 ~ ay > : ^ ^ay ¼ c12 c þ c11 b þ c13 df

ð6Þ

2



a11 a21

3 2 3 2ðCf þ Cr Þ 2ðlf Cf  lr Cr Þ 2Cf 2 3  1  6   7 6 mvx 7 c11 mVx mVx2 a12 6 7 b1 7 4 5 ¼6 ¼6 7; 4 2lf Cf 5; c12 a22 4 2ðlf Cf  lr Cr Þ 2ðl2f Cf þ l2r Cr Þ 5 b2 c13 Iz Iz 3 Iz Vx 2 2ðCf þ Cr Þ m 6 7 2ðlf Cf lr Cr Þ 7 ¼6 4 5 mVx 2Cf m

~ ¼bb ^ and where qi(i = 1, 2, 3, 4) are the control factor of the observer, ~c ¼ c  ^c; b ~ ay ¼ ay  ^ay can be described as 8   ~  q1 j~cj12 signð~cÞ  q3 ~ > ay < ~c_ ¼ a22~c þ a21 b   1 _~ ~  q2 j~cj2 signð~cÞ  q4 ~ b ¼ a12~c þ a11 b ay > : ~ ~ay ¼ c12~c þ c11 b

ð7Þ

The Lyapunov function V ð~cÞ ¼ 12 ~c2 is constructed to verify the stability of the SMO. The time derivative of the V ð~cÞ is h i ~  q1 j~cj12 signð~cÞ V_ ð~cÞ ¼ ~c Q~c þ W b

ð8Þ

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255

where Q = a22-q3c11, W = a21-q3c12. The following function will be obtained if q1 is large enough.    ^ dQ~ce þ W b  þ j1 q1  ð9Þ 1 j~cj2 and j1 > 0, it can be obtained   h  i ~  dQ~ce þ W b ^ þ j1 signð~cÞ V_ ð~cÞ  ~c Q~c þ W b   j1 j~cj  0

ð10Þ

According to the Lyapunov stability, ~c is stable. Then, ~c ¼ 0 and ~c_ ¼ 0 can be obtained. The first subsystem in (7) can be expressed as h

1

j~cj2 signð~cÞ

i eq

¼

~ Wb q1

ð11Þ

substituting (11) into the second subsystem of (7) ~_ ¼ b

 q2 ða21  q3 c12 Þ ~ a11   q4 c12 b q1

ð12Þ

 ~ ¼b ~b. ~_ It can be concluded that b ~ will converge to zero by According to V_ b properly selecting observer gains qi(i = 2, 3, 4). 3.2

Control Layer Design: Upper Controller

The Vxr is acquired from the pedal signal. The reference velocity of the vehicle is expressed as Z Vxr ¼ V0 þ

t

axr dt

ð13Þ

t0

For simplification, the PI controller is adopted to obtain the longitudinal forces Fx. The ASMC control is used to enhance the system robustness against the parameters variation. The yaw rate and sideslip angle are selected to design the sliding surface ^b Þ s ¼ ðc  cd Þ þ nðb d

ð14Þ

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where n is weight factor which is a positive value. For reducing the chattering and improving the transient performance of the system, an adaptive reaching law [17] is chosen as. s_ ¼ ejsjr edjsj satðs=MÞ   satðs=MÞ ¼



aþ 1þ

k 1 k xk 1

s  a edjsj

ð15Þ

s; if jsj  D sgnðs=MÞ; if jsj [ D

where e > 0, 0 < r < 1, k > 0, x > 0, d > 0, x is the system status variable. According to the equation in (3), the controller Mz is expressed as 



^_  bÞ _  2 lf Cf  lr Cr b  Mz ¼ Iz ½_cd þ fðb þ



aþ 1þ

k 1 k xk1

 2 l2f Cf þ l2r Cr vx

c þ 2lf Cf df þ ejsjr edjsj satðs=MÞ

s  a edjsj

ð16Þ Define Lyapunov function as 1 V ðsÞ ¼ s2 2

ð17Þ

The derivative of system (12) is expressed as _ VðsÞ ¼ s_s ¼ ejsjr edjsj satðs=MÞs 

 aþ 1þ

k 1 k xk 1

s2  a edjsj

ð18Þ

_ Noting that 1 þ kx1k  a [ 0, V\0, according to Lyapunov theory, the system is 1 stable. The reaching speed is decided by the distance of the sliding mode switching function to the balance point. When the |s| is increasing, the control variables of the system reach to |s| mainly according to the means of the index −k=a. When ||x||1 is close to |s|, variable velocity term −e|s|rsat(s=Δ) plays a key role which decreases gradually and approaches zero. Therefore, this adaptive controller can adjust dynamically according to the change of the system states which can moderate the chattering and shorten the reaching time.

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3.3

257

Fault Tolerant Control Allocation (FTCA)

In this section, the FTCA is used to assign the desired torques to the motors even the motor faults. It can be described as   Fx ð19Þ ¼ Beq ½ ufl ufr url urr T Mz In order to keep the vehicle stay away from the edge of losing its stability, we should keep the tire in the boundary of its adhesive force. Hence, the optimal objective is minimize the tire utilization rate, which is expressed as X X F2 X ^k2 ui 2 xi i gi ¼ ¼ ð20Þ J1 ¼ R2 lFzi ðlFzi Þ2 where Fzi and l represent the dynamic tire load and the road adhesion coefficient respectively. The four control signal u of the actuators need to submit to (19) and the output constraint. Hence, the FTCA problem is expressed as X   2 J ¼ J1 þ J2 ; J1 ¼ g; J2 ¼ c1 Beq u  v 2 ð21Þ where v = [Fx Mz]T, The positive constant c1 is weight factor. Transform problem (21) into a standard quadratic (QP) formulation. First, let X ¼ u  u; Xmax ¼ u  u

ð22Þ

By extending J1, J2, they can be rewritten as ^k 2 X þ u2

 2 J1 ¼ 2 ; J2 ¼ c1 Beq X  ap 2 lFz R

 where ap ¼  Beq u  v It can be obtained as following. J1 ¼

ð23Þ

^k2 X T X ^k2 X T u ^k2 uT X ^k2 uT u þ þ þ R2 ðlFz Þ2 R2 ðlFz Þ2 R2 ðlFz Þ2 R2 ðlFz Þ2

J2 ¼ X T BTeq cT1 c1 Beq X  X T BTeq cT1 c1 ap  aTp cT1 c1 Beq X þ aTp cT1 c1 ap

ð24Þ

Let H1 ¼

^k 2 R2 ðlFz Þ

2

; C1T ¼

^k2 uT R2 ðlFz Þ

2

; R1 ¼

^k2 uT u R2 ðlFz Þ2

H2 ¼ BTeq cT1 c1 Beq ; C2T ¼ aTp cT1 c1 Beq ; R2 ¼ aTp cT1 c1 ap

ð25Þ

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where H1 ; H2 2 R44 , C1T ; C2T 2 R14 , R1 ; R2 2 R11 , therefore, it can be obtained that H ¼ 2ðH1 þ H2 Þ; C T ¼ C1T þ C2T ; R ¼ R1 þ R2

ð26Þ

then (21) becomes a quadratic programming(QP) problem as 1 min J ¼ X T HX þ CT X þ R 2 s:t: 0  X  Xmax

ð27Þ

The optimization matrix H and Beq can adjust automatically according to the fault factor which can maintain the vehicle stability when the actuator fails.

4 Simulation Result In this section, two kinds of faulty conditions are simulated by Simuink-Carsim. The DDEV parameters are listed as follow (Table 1). Table 1. Parameters of the DDEV Parameter M d If Ir Iz R Cf,r

4.1

Value 1423 kg 1.739 m 1.04 m 1.56 m 1523 kg∙m2 0.311 m 25796 N/rad

Straight Line Simulation

The DDEV drives at 80 km/h on straight road which road friction factor is 0.2. At 4 s, the front-right wheel fault factor qfr changed from 0 to 0.4. Then qfr changed from 0.4 to 1 at 12 s. The states of the 4WID-EV can be seen from Fig. 3. Figure 3(a) shows that the yaw rate without control has changed as soon as fault factor changes, but the yaw rate with control changes in a small extent. This faulty yaw changing may mislead the driver into wrong operation which makes the vehicle lose the stability. Because of the decrease of the torque output, the speed of the vehicle without control reduced for twice in Fig. 3(b). Figure 3(c) shows the motor torques with control. When the fault factor changes first, the other three motor torques updates within a low extent to keep torques on the both sides equal. At 12 s, the system distributes more torque to the rear-right hub motor to keep the torques on both sides equal. Figure 3(d) shows the vehicle displacements with/without control. It can conclude that the DDEV with control can trace the reference trajectory without driver’s correction but the system without control deviates from the reference trace.

Fault Tolerant Control Allocation Based on Adaptive Sliding Mode Control Reference

Yaw rate deg/s

0

With control

80.01

Without control

Longitudinal velocity km/h

0.02

-0.02 -0.04 -0.06 -0.08 -0.1 -0.12 0

5

10 Time s

15

80 79.99 79.98 79.97 Reference

79.96

With control

Without control

79.95 79.94 0

20

(a) Yaw rate

5

10 Time s

15

20

(b) Longitudinal velocity 1

16 Tfl Trl

12

Tfr Trr

Lateral displacement m

14

Torque N*m

259

10 8 6 4 2

ρ fr = 0.4

0

ρ fr = 1

-2 0

5

10 Time s

15

20

(c) Motor torques with ASMC-FTCA

0 -1 -2 -3 -4 -5

Reference

With control

Without control

-6 0

100 200 300 Longitudinal displacement

m

400

(d) Traveling trajectory

Fig. 3. States of the DDEV with straight line.

4.2

Single Lane Change Simulation

On the same low tire-road friction coefficient road, the DDEV drives at 80 km/h with single lane change. Under this condition, the front-right wheel fault factor qfr is changed from 0 to 0.4 at 4 s. Then, qfr is changed from 0.4 to 1 at 7 s. The states of the DDEV are given in Fig. 4. Figure 4(a) shows that yaw rate without control has changed as soon as fault factor changes, but the yaw rate with control changes in a small extent. Figure 4(b) shows that there is a slight change of the sideslip angle in the response without control. It has to be noted that the steering angle is preset so the situation of driver unreasonable operation didn’t happen in the simulation, that is why the yaw rate and sideslip angle without control change in a slight extent when the fault happens. Figure 4(c) shows the motor torques outputs with FTCA. When the fault factor changes first, the other three motor torques updates to keep the torques on both side equal. At 7 s, the vehicle with ASMCFTCA control distributes more torque to the rear-right hub motor to compensate the torque loss of the right side. Figure 4(d) shows the vehicle displacements with/without control. The vehicle without control is far away from the reference trajectory but the vehicle with control can trace the anticipated trajectory so that the driver have no necessity to adjust the front steering angle.

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5 Conclusion This paper has proposed an ASMC-FTCA strategy for DDEV to enhance the stability of the vehicle on low tire-road friction coefficient road when actuator fails. The sideslip angle of the DDEV is estimated by state observer. An ASM controller with a fractional power term reaching law is used to enhance the reliability of the system in critical situations. In the lower controller, the FTCA is used to cope with the motor fault. The simulation shows that the ASMC-FTCA can maintain the safety of the DDEV in critical situations.

References 1. Wang Y, Yu S, Yuan J, Chen H (2018) Fault-Tolerant control of electric ground vehicles using a triple-step nonlinear approach. IEEE-ASME Trans Mechatron 23(4):1775–1786 2. Zhou H, Jia F, Jing H, Liu Z, Güvenç L (2018) Coordinated longitudinal and lateral motion control for four wheel independent motor-drive electric vehicle. IEEE Trans Veh Technol 67 (5):3782–3790 3. Wang R, Wang J (2014) Actuator-redundancy-based fault diagnosis for four-wheel independently actuated electric vehicles. IEEE Trans Intell Transp Syst 15(1):239–249 4. Gao Z (2015) Fault estimation and fault tolerant control for discrete-time dynamic systems. IEEE Trans Ind Electron 62(6):3874–3884 5. Wang R, Wang J (2011) Fault-tolerant control with active fault diagnosis for four-wheel independently driven electric ground vehicles. IEEE Trans Veh Technol 60(9):4276–4287

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6. Zhang G, Zhang H, Huang X, Wang J, Yu H, Graaf R (2016) Active fault-tolerant control for electric vehicles with independently driven rear in-wheel motors against certain actuator faults. IEEE Trans Control Syst Technol 24(5):1557–1572 7. Guo B, Chen Y (2018) Robust adaptive fault tolerant control of four wheel independently actuated Electric vehicles. IEEE Trans Ind Inf. 64(8):1109–1119 8. Yang H, Cocquempot V, Jiang B (2010) Optimal fault-tolerant path tracking control for 4WS4WD electric vehicles. IEEE Trans Intell Transp Syst 11(1):237–243 9. Zhang D, Liu G, Zhou H, Zhao W (2018) Adaptive sliding mode fault tolerant coordination control for four wheel independently driven electric vehicle. IEEE Trans Ind Electron 65 (3):9090–9100 10. Xu P, Hou Z, Guo GF, Xu G, Cao BG, Liu ZL (2011) Driving and control of torque for direct-wheel-driven electric vehicle with motors in serial. Expert Syst Appl 38(1):80–86 11. Ding S, Liu L, Zheng WX (2017) Sliding mode direct yaw-moment control design for inwheel electric vehicles. IEEE Trans Ind Electron 64(8):6752–6762 12. Li L, Lu Y, Wang R et al (2016) A 3-dimensional dynamics control framework of vehicle lateral stability and rollover prevention via active braking with MPC. IEEE Trans Ind Electron. https://doi.org/10.1109/TIE.2016.2682024 13. Yang Z, Zhang D, Sun X et al (2018) Adaptive exponential sliding mode control for a bearingless induction motor based on a disturbance observer. IEEE Access 6:1938–1948

Research on Sliding Mode Active Disturbance Rejection Control and Thrust Allocation of Dynamic Positioning System Zaiji Piao and Chen Guo(&) School of Marine Electrical Engineering, Dalian Maritime University, Dalian, China [email protected]

Abstract. In order to further improve the control ability of dynamic positioning ship or platform under complex sea conditions, the controller and thrust allocation system of dynamic positioning are designed. A simplified three-degreeof-freedom MMG mathematical model is established for the pod full-rotation propeller used in most dynamic positioning ships. On the basis of retaining the advantages of active disturbance rejection control, the sliding mode variable structure control method is adopted to design the control law. The objective function and constraints of thrust allocation are designed based on the lowest energy consumption, and the sequential quadratic programming algorithm is used to calculate the real-time thrust allocation of each propeller. The simulation results of the ship dynamic positioning system in MATLAB show that the controller can maintain the stability and robustness of the system under the condition of external environment disturbance, and the results of the thrust allocation are reasonable, which has a certain reference value for the further study of the ship dynamic positioning control system. Keywords: Sliding mode control
Dynamic positioning system Thrust allocation
Sequential quadratic programming




1 Introduction According to the definitions of IMO (International Maritime Organization), norwegian classification society and so on, ship dynamic positioning means that a ship uses the propulsion generated by its own propulsion system to resist the disturbances of wind, wave, current and other external environmental disturbances, so that the ship can automatically maintain a fixed position on the sea surface or track an expected trajectory accurately [1]. Fossen et al. classified and analyzed the existing thrust allocation strategies in detail [2]. Webster transforms the thrust allocation problem into a linear programming problem [3]. ADRC is a new nonlinear control algorithm proposed by Han on the basis of nonlinear PID in recent years. It has strong anti-interference ability and does not depend on the precise model of the control object. It solves the problems existing in modern control. LEI applied ADRC to the control system of ship dynamic positioning [4]. Tracking differentiator was used to arrange the transition process. Although it © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 262–269, 2020. https://doi.org/10.1007/978-981-32-9050-1_30

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solved the contradiction between overshoot and response speed, the process of realization is more complicated and the process of parameter setting was more complicated. Sliding mode variable structure control does not depend on specific mathematical model, and has strong robustness [5], but there are some chattering problems, which affect the practical application. Based on the advantages of sliding mode control and ADRC, this paper presents an auto-disturbance rejection control method for semi-submersible ship dynamic positioning. The extended state observer (ESO) was used to estimate the unknown states and uncertainties of the dynamic positioning control system. The sliding mode control law was designed to improve the control performance and effectively suppress the chattering problem of the traditional sliding mode control.

2 Mathematical Model of Marine Dynamic Positioning System 2.1

Ship Mathematical Model

The equation for dynamic positioning of ships at low frequencies can be written as follows:


g_ ¼ RðwÞV M V_ þ DV ¼ sT þ sW þ w

ð1Þ

Among them, g ¼ ½xG0 ; yG0 ; wT is the position vector of the ship, that is, the position of the ship in inertial coordinates and the heading angle; V ¼ ½u; v; r T is3the 2 cos w sin w 0 velocity vector of the ship’s low-frequency motion; RðwÞ ¼ 4 sin w cos w 0 5 is 0 0 1 the transformation matrix between the position coordinates of ships and the state of motion of ships; sT ¼ ½XT YT NT T is the control in 2 3quantity 2 calculated by the controller 3 Xwave þ Xwind þ Xcurrent Xw the dynamic positioning system; sW ¼ 4 Yw 5 ¼ 4 Ywave þ Ywind þ Ycurrent 5 is the Nw Nwave þ Nwind þ Ncurrent disturbance force and moment caused by wind, wave and current in the marine environment, dynamic disturbance of the 2 and w is the unmodeled 3 2 system; Inertia matrix 3 m  Xu_ Xu 0 0 0 0 M¼4 0 m  Yv_ Yv Yr 5. Yr_ 5; Damping matrix D ¼ 4 0 0 Nv_ Izz  Nr_ 0 Nv Nr In this paper, we calculate the M and D values based on a series of linear hydrodynamic equations summarized by Clarke.

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A Model of Disturbance Force on Ship

The stability of dynamic positioning ship in low frequency motion, that is to say, the stability and accuracy of dynamic positioning are mainly affected by uncertain environmental disturbances in the ocean. Among them, the ocean disturbance force which can cause the position fluctuation of ship dynamic positioning system usually has the second-order dynamic force of wave, current and average wind. Their expressions can be described as [6]: 8 < XW ¼ Fe cosðbe  wÞ YW ¼ Fe sinðbe  wÞ ð2Þ : NW ¼ lx sinðbe  wÞ  ly cosðbe  wÞ


pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 þ Y2 Fe ¼ XW W be ¼ w þ tan1 ðYW =XW Þ

ð3Þ

Among them, Fe is a steady force which changes  slowly; be is the average angle value of the external disturbance to the ship; lx ; ly is regarded as the action point of the total disturbance on the ship. Marine environmental disturbance is a variable that varies with the course of a ship. For the unmodeled dynamic disturbance of ship dynamic positioning system w ¼ ½w1 ; w2 ; w3 T , the following mathematical expressions are used to describe:


w ¼ JT ðgÞb b_ ¼ T1 b þ Eb xb b

ð4Þ

Among them, b 2 R3 represents deviation force and moment, Eb ¼ diagfEb1 ; Eb2 ; Eb3 g, xb is Gaussian white noise and Tb is a diagonal matrix, including forward pressure time constant.

3 Sliding Mode Active Disturbance Rejection Control The structure of ADRC is shown in Fig. 1 [7], its control law contains non-linear function and its parameters are not easy to determine. Because the process of designing the control law by sliding mode control technology is simple and easy to implement, this paper considers sliding mode control technology to design the control law of ADRC. The control principle is to design the switching superplane of the system according to the desired dynamic characteristics of the system, and to switch the system state from the other place to the superplane by sliding mode controller [8].

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The steps of designing sliding mode ADRC controller are: (1) The input and output signals of the system and the disturbances are observed and estimated reasonably by designing TD and ESO. (2) Design the switching function s(x), so that the sliding mode determined by it is asymptotically stable and has good dynamic quality. (3) Design u ðxÞ, thus forming a sliding mode area on the switching surface. This control law is used as the control law of ADRC. It is known from (1): V_ ¼ M 1 DV þ M 1 ðsT þ sD Þ

ð5Þ

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ð6Þ

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ð7Þ

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ð8Þ

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In summary, the three-dimensional sliding mode control law can be calculated as follows: u0 ¼ ðRðwÞM 1 Þ1 ðC e_ þ esgnðsÞ þ ks þ RðwÞM 1 DRðwÞ1 g_  RðwÞM 1 sD Þ

ð9Þ

Thus, the control equation are: e ¼ z1  y z_ 1 ¼ z2  b01 e

9 > > > > > > > > =

z_ 2 ¼ z3  b02 falðe; a1 ; d1 Þ þ bu > z_ 3 ¼ b03 falðe; a2 ; d2 Þ > > > > > e1 ¼ x1  z 1 ; e2 ¼ x2  z 2 > > ; u ¼ u0  z3 =b

ð10Þ

4 Thrust Allocation The thrust allocation system is an extremely important part of the DPS. Its function is to allocate the force and direction of each thruster according to the force and moment calculated by the controller, so that the propulsion system can keep the ship in a fixed position or track a fixed track [9–11] (Fig. 2).

Fig. 2. Thrust optimization control system

The ship has two pairs of propeller: one is the SSP pod propeller, as the main thruster for the ship, and the other is the side thruster (Fig. 3).

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Fig. 3. Propeller layout

The force and moment of a ship can be calculated according to the following formulas: 8 X ¼ F1 cosða1 Þ þ F2 cosða2 Þ > > > < Y ¼ F sinða Þ þ F sinða Þ þ F þ F 1 1 2 2 3 4 ð11Þ 1 > N ¼ ðF1 cosða1 Þ  F2 cosða2 ÞÞð2 L1 Þ > > : ðF1 sinða1 Þ þ F2 sinða2 ÞÞð12 L2 Þ þ ðF3 þ F4 Þð12 L3 Þ The objective function and constraint conditions designed are as follows [12–14]: 8 2 4 3 P P > > > min f ðF; dÞ ¼ PW þ Q ðai  ai1 Þ2 ; W ¼ ki Fi2 > > > i¼1 i¼1 > > > 2 > P > > > s:t: XT  Fi cos ai ¼ 0 > > > i¼1 > > > 2 > P > < YT  Fi sin ai  F3  F4 ¼ 0 ð12Þ i¼1 > > 1 > NT  (F1 cos(a1 Þ  F2 cos(a2 ÞÞð2 L1 Þ  ðF1 sin(a1 Þ > > > > > + F2 sin(a2 ÞÞð12 L2 Þ  ðF3 + F4 Þð12 L3 Þ ¼ 0 > > > > > Fmin  Fi  Fmax > > > > > amin  ai  amax > > > : Damin  ai  ai1  Damax

5 Simulation of Dynamic Positioning System This paper takes the ship named Taiankou as an example for simulation. After calculation, 2the M and D of the model are3 as follows:2 3 0:3418 0 0 0:0044 0 0 M=4 0 0:3336 0:0007 5 and D = 4 0 0:0186 0:0029 5. 0 0:0005 0:0228 0 0:0051 0:0024

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The given system input is: x = 50, y = 50, w = 10, Disturbance of wind, wave and current:   Fe ¼ 300; be ¼ 120 sinðsin 0:3tÞ; lx ; ly ¼ ð20m; 5mÞ; Tb ¼ diagf1000; 1000; 1000g, Eb ¼ diagf1; 1; 1g, the simulation results are: From the above simulation results, we can see that this sliding mode active disturbance rejection control can complete the control target, and the thrust allocation module has also obtained reasonable results. This is of great significance to the maneuverability of double pod propulsion ship, and gives a complete control mode about dynamic positioning system (Figs. 4 and 5). Acknowledgement. Research supported by supported by National Natural Science Foundation of China (Nos. 51879027, 51579024, 61374114, 51809028) and the Fundamental Research Funds for the Central Universities (DMU No. 3132016311).

References 1. Sørensen AJ (2011) A survey of dynamic positioning control systems. Ann Rev Control 35 (1):123–136 2. Johansen TA, Fossen TI (2013) Control allocation—a survey. Automatica 49(5):1087–1103 3. Webster WC, Sousa J (1999) Optimum allocation for multiple thrusters. In: The ninth international offshore and polar engineering conference, International Society of Offshore and Polar Engineers 4. Lei Z, Guo C, Fan Y (2015) Dynamic positioning system based on active disturbance rejection technology. J Ocean Univ China 14(4):636–644 5. Shtessel Y, Edwards C, Fridman L et al (2014) Sliding mode control and observation. Springer, New York 6. Fossen TI, Strand JP (2001) Nonlinear passive weather optimal positioning control (WOPC) system for ships and rigs: experimental results. Automatica 37(5):701–715 7. Han J (2009) From PID to active disturbance rejection control. IEEE Trans Ind Electron 56 (3):900–906 8. Tannuri EA, Agostinho AC, Morishita HM et al (2010) Dynamic positioning systems: an experimental analysis of sliding mode control. Control Eng Pract 18(10):1121–1132 9. De Wit C (2009) Optimal thrust allocation methods for dynamic positioning of ships 10. Sørdalen OJ (1997) Optimal thrust allocation for marine vessels. Control Eng Pract 5 (9):1223–1231 11. Veksler A, Johansen TA, Skjetne R (2012) Thrust allocation with power management functionality on dynamically positioned vessels, 2012 American Control Conference (ACC), pp 1468–1475 12. Tannuri EA, Morishita HM (2006) Experimental and numerical evaluation of a typical dynamic positioning system. Appl Ocean Res 28(2):133–146 13. Johansen TA, Fossen TI, Berge SP (2004) Constrained nonlinear control allocation with singularity avoidance using sequential quadratic programming. IEEE Trans Control Syst Technol 12(1):211–216 14. Härkegård O (2004) Dynamic control allocation using constrained quadratic programming. J Guidance Control Dyn 27(6):1028–1034

Research on Indoor Positioning Method Based on Improved HS-AlexNet Model Libiao Zhang(&), Rui Zhao, Yuqing Liu, Xinyu Yang, and Shipeng Li School of Information Science and Technology, Northeast Normal University, Changchun 130117, Jilin, China [email protected]

Abstract. Scene recognition is the key to achieving accurate and fast indoor positioning. Deep network has become a research central issue lately with its outstanding performance. This paper puts forward an advanced AlexNet network model combined with Harris feature detection, which guides the image processing of the original model according to the detected Harris characteristics, it reduces the randomness error and improves the generalization ability and robustness of the model. In addition, for the campus indoor scene positioning environment, the original structure of the AlexNet network model and the data augmentation method are improved, so that the positioning model can cope with the complex and variable positioning environment, and its accuracy and speed reach a high level. The method can be combined with the existing mainstream visual indoor positioning method to enhance the accurateness and speed of positioning system. Keywords: Scene recognition
AlexNet network model Data augmentation
Indoor positioning


Harris features


1 Introduction Recently, with the progress of vision indoor positioning technology [1, 2], scene recognition occupies an important position [3, 4] in indoor positioning and gains widespread attention. Scene recognition is judging the category of scene by observing the content contained in the image [5]. Deep learning has greatly improved the recognition rate of scene classification and has become a research trend. Krizhevsky [6] applied the AlexNet convolutional neural network to ImageNet image classification, using random clipping and mirroring data augmentation to achieve good results; Zeiler [7] proposed ZF Net based on the optimization of AlexNet architecture; Karen [8] increased the network depth by adding more convolution layers based on the network structure of ZF Net, which proved that depth is beneficial to classification accuracy; SVETLANA [9] proposes a multi-scale disordered pooled convolutional neural network (MOP-CNN), which combines multi-scale features with CNN to improve the classification and matching effect of the model on variable scenes; Zhong [10] was inspired by Bolei to propose a supervised data augmentation method. According to the appearing probability of the target object at different positions, © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 270–277, 2020. https://doi.org/10.1007/978-981-32-9050-1_31

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different clipping regions were selected to improve the recognition accuracy. Inoue [11] proposed an efficient data augmentation method, which can expand the training set size from N to N*N. Based on the above analysis, inspired by the supervised data augmentation [13, 14] proposed by Q Zhong et al., this paper puts forward an advanced AlexNet network model combined with Harris feature [12].

2 Related Work 2.1

AlexNet Network Model

In 2012, the effect of the AlexNet model proposed by Krizhevsky et al. in the ImageNet ILSVRC-2012 image classification problem is much higher than the traditional method. It has become the classic to open deep learning. The structural model of AlexNet is shown in Fig. 1. The model is divided into 7 layers, including 5 layers of convolutional layers and 2 layers of fully connected layers.

Fig. 1. AlexNet network model

In order to prevent the over-fitting phenomenon, AlexNet randomly intercepts the 224 * 224 area from the original image of 256 * 256, and the horizontally flipped image. The use of data enhancement can greatly reduce over-fitting and improve generalization ability. When making predictions, it takes 5 positions in the picture and flips left and right. AlexNet uses the ReLU function as the activation function of the network to obtain the feature map of each layer. Other activation functions may have supersaturation characteristics during the gradient descent. The mathematical expression of the ReLU is: f ðxÞ ¼ maxð0; xÞ

ð1Þ

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Local standardization helps generalization. AlexNet uses LRN local response normalization. The formula is as follows: bix;y ¼ aix;y =ðk þ a

XminðN;i þ n=2Þ j¼maxð0;i2=nÞ

ðaix;y Þ2 Þb

ð2Þ

Where aix;y denotes the value of the ith convolution kernel passing through the ReLU unit at the ðx; yÞ position, N is the number of kernels of the layer, and k; n; a; b are preset hyperparameters. The Dropout strategy prevents the model from overfitting by modifying the structure of the neural network itself. For each hidden layer’s output, it is set to 0 with a 50% probability, no longer acting on the forward or backward process. Such weight update does not depend on the joint action of the fixed relationship implicit nodes, preventing certain feature weights from being valid only under certain characteristics. AlexNet uses this strategy in the last two fully connected layers. 2.2

Harris Feature Detection

The Harris detection algorithm is based on the H. Moravec algorithm by Chris and Mike. The eigenvalue of the autocorrelation matrix M is the first-order curvature of the autocorrelation function. The Harris corner detection algorithm uses the row and column curvature value of a point in the image as the basis for judging whether it is a corner point. It is assumed that the rectangular window W centered on the target pixel is shifted in any direction ðu; vÞ into Eðu; vÞ, which is defined as shown below: Eðu; vÞ ¼

X x;y

wðx; yÞ½I ðx þ u; y þ vÞ  I ðx; yÞ2

ð3Þ

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Eðu; vÞ ¼

x;y

¼

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Ix2 Ix Iy

Ix Iy Iy2

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ð4Þ

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X

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Ix Iy Iy2

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The matrix M is an autocorrelation matrix whose eigenvalues (set to k1 , k2 ) reflect the curvature of the autocorrelation function Eðu; vÞ. When both eigenvalues are small, it indicates a flat area; one eigenvalue is larger, and the other is smaller, it represents an edge; when both eigenvalues are larger, it is a corner point.

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3 Feature Detection HS-AlexNet Improved Model Because the AlexNet network model is designed for the data set of 1000 categories of the ImageNet [15] contest, including many aspects such as biology, transportation, life, and scenes. It has a strong generalization ability for generalized classification problems, but it is difficult to show good performance in indoor location. 3.1

Feature Detection and Data Augmentation

For the generalized classification problem, the AlexNet model uses random crop-ping and horizontal flip mirroring to augment the data [16], which reduces the occurence of overfitting and improves the generalization ability of the model. However, the edge of the image with discriminative meaning is randomly cropped. In order to reduce the possibility of occurrence of such randomness errors, cropping preserves more discriminative image regions, detects the image Harris corner feature firstly, and selects the region with the largest or most dense feature as the crop-preserving region, improving the semantics of the image set. In the scene recognition problem, image data acquisition is affected by many factors such as illumination changes, noise jitter and sensor rotation changes. The original AlexNet network model has a poor fitting effect in the scene recognition problem, and it is difficult to obtain accurate positioning results in the actual application scenario. Aiming at the above problems, this paper proposes to improve the data augmentation mode of the original model, and use the random rotation fixed range angle, changing the image brightness, and the random distance within the fixed range to add the fuzzy interference to expand the training set. 3.2

Model Structure Design and Optimization

For the small-resolution campus indoor scene dataset, the original network model structure is difficult to effectively extract effective local features, resulting in low positioning accuracy. In response to the above problems, combined with the characteristics of the campus indoor scene dataset, the original model structure is adjusted and optimized. For the 127 * 127 campus indoor scene image set, the convolution kernel 11 * 11 of the first convolutional layer is too large to express the effective information of the local features. Therefore, the size of the convolution kernel of the first layer convolutional layer is modified to be 7 * 7, and a better feature extraction effect is obtained. The AlexNet network model is subjected to convolution and pooling to obtain a feature map of size 6 * 6. Input to the fully connected layer will generate a large number of network parameters, and there is a tendency to overfit. So, this paper adds a pooling layer after the 5th the pooling layer, the pooling operation scale is 3 * 3, and the operation step is 2. Simultaneously, after the dropout layer behind the second fully connected layer, a layer of dropout layer is added to further alleviate the over-fitting phenomenon and reduce the computational complexity.

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4 Analysis of the Experiment and Results 4.1

Experiment Data

Common scene datasets include the Scene15 dataset, the SUN397 dataset, the SUN Attribute dataset [17] and the MIT Indoor67 dataset [18] The experiment selects common indoor scenes in the campus from three different data sets, including classrooms, computer rooms, laboratories, libraries and corridors. Simultaneously, manually collected campus indoor scene images are added in each category, and each category has 500 images, which constitute a campus indoor scene School dataset. The dataset is divided into training, verification and test set according to the ratio of 8:1:1. The specific situation of the School data set is as follows (Table 1). Table 1. School dataset. Heading level MIT 67 SUN attribute SUN397 Manual collection Sum Training set 400 175 1159 267 2001 Verification set 50 25 147 33 255 Testing set 50 20 142 32 244 Sum 500 220 1448 332 2500

4.2

Experiment Result

The Harris corner feature is extracted from the School dataset, and the cutting mode that retains the maximum number of feature points is selected for cropping. The feature point extraction and cropping result are shown in Fig. 2.

Fig. 2. Feature point extraction.

The data set after the cropping process is 10 times of the original data by randomly rotating the arbitrary angle, decentralized, horizontal and vertical directions, and the image is enhanced as shown in Fig. 3.

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Fig. 3. Data augmentation.

The Alexnet model was applied to the School dataset test with an accuracy of 73.2%. The convolution kernel size n of the first layer convolutional layer of the network model structure is adjusted to 7,9, and the model accuracy is improved to 78.4% and 73.8%. Therefore, when the first layer convolution kernel size is 7 * 7, the model accuracy rate is high, and the experimental results are as follows (Fig. 4).

Fig. 4. Different convolution kernel size.

After the pooling layer is added, the accuracy rate is improved to 78.8%, but there is an over-fitting trend. After the dropout layer is added, the accuracy is stable at about 74.8%, the network is more stable, and the convergence speed is higher (Fig. 5). Based on the Harris feature detection, the angles of random rotation angles are 0, 10, 20, and 30, respectively. The decentering processing and the random distance in the fixed range are added to the fuzzy interference mode for data augmentation, and the accuracy is improved. The result is shown in Fig. 6. The smaller the degree of rotation of the image, the faster the convergence is achieved, but the tendency to overfit is more. The random rotation angle is within 30°, and the fuzzy interference is randomly added within the translation distance of 20 px. The effect is optimal, there is no over-fitting trend, and the accuracy rate is increased to 79.2%.

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Fig. 5. Adding pooling layer and dropout layer.

Fig. 6. Random rotation angles.

5 Conclusion Computer vision positioning is a research hotspot and development trend in indoor positioning. Scene recognition is a key step in visual indoor positioning. An indoor scene localization method based on feature detection and AlexNet model is proposed in this paper,. The indoor scene recognition based on neural network is applied to indoor positioning to reduce the influence of random error factors generated in the positioning process on the model, improve positioning accuracy and stability, and achieve precise semantic positioning. Experiments show that the improved model data augmentation method and model structure for indoor positioning environment can enhance the accurateness and effect of positioning in effect. In the future, scene recognition can be further refined into indoor positioning to achieve a more refined level of indoor positioning of the scene.

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References 1. Kim JY, Yang SH, Son YH (2016) High-resolution indoor positioning using light emitting diode visible light and camera image sensor. LET Optoelectron 10:184–192 2. Kawaji H, Hatada K, Yamasaki T (2010) Image-based indoor positioning system: fast image matching using omnidirectional panoramic images. In: International workshop on multimodal pervasive video analysis, pp 1–4 3. Wei H, Wang L (2018) Understanding of indoor scenes based on projection of spatial rectangles. Pattern Recogn 81:497–514 4. Madokoro H, Yamanashi A, Sato K (2013) Unsupervised semantic indoor scene classification for robot vision based on context of features using Gist and HSV-SIFT. Pattern Recogn Phys 1:93–103 5. Pandey M, Lazebnik S (2011) Scene recognition and weakly supervised object localization with deformable part-based models. In: International conference on computer vision, vol 23, pp 1307–1314 6. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: International conference on neural information processing systems, vol 60, pp 1097–1105 7. Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European conference on computer vision, vol 8689, pp 818–833 8. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Comput Sci 9. Gong YC, Wang LW, Guo RQ (2014) Multi-scale orderless pooling of deep convolutional activation features. In: European conference on computer vision, pp 392–407 10. Zhong Q, Li C, Zhang Y (2017) Cascade region proposal and global context for deep object detection. In: IEEE conference on computer vision and pattern recognition 11. Inoue H (2018) Data augmentation by pairing samples for images classification. In: International conference on learning representations 12. Harris C, Stephens M (1988) A combined corner and edge detector. In: Proceedings of fourth alvey vision conference, pp 147–151 13. Xue DX, Zhang R, Feng H (2016) CNN-SVM microvascular morphological type recognition with data augmentation. J. Med. Biolog. Eng. 36:755–764 14. Guo J, Gould S (2015) Deep CNN ensemble with data augmentation for object detection. Comput Sci 15. Russakovsky O, Deng J, Su H (2015) ImageNet scale visual recognition challenge. Int J Comput Vis 115:211–252 16. Jia S, Wang P, Jia P, Hu S (2018) Research on data augmentation for image classification based on convolution neural networks. In: Chinese automation congress, pp 4165–4170 17. Patterson G, Hays J (2012) SUN attribute database: discovering, annotating, and recognizing scene attributes. Comput Vis Pattern Recogn 157:2751–2758 18. Wang Z, Wang L, Wang Y (2017) Weakly supervised PatchNets: describing and aggregating local patches for scene recognition. IEEE Trans Image Process 26:2026–2041

A Modified Energy and Signal Coordination Control Strategy for a Robotic System Yu Wang, Haisheng Yu(&), Jinpeng Yu, Herong Wu, and Xudong liu Qingdao University, Qingdao 266071, China [email protected]

Abstract. A new smooth switching control strategy is devised to ameliorate the position tracking performance of the robotic arms. Firstly, a signal controller is based up the inverse of the modified backstepping sliding model control is project to improve the dynamic property of the system. An energy controller based on port-controlled Hamiltonian system (PCH) was designed and integrated control was introduced as compensation to improve the steady-state performance of the system. Finally, a smooth switching function based on tracking error is devised to achieve smooth switching between signal control and energy control. The permanent magnet synchronous motor (PMSM) motor model is introduced in the design to make the controller more in line with the actual demand, and it is decomposed into position controller and torque controller, which reduce the difficulty of implementation. In the last part of the paper, a two-degree-of-freedom robot is taken as an example to verify the feasibility and advantages of the algorithm. Keywords: Robotic manipulator


PMSM
Hybrid control

1 Introduction In the actual robot dynamics, there are many adverse factors such as dynamic coupling, high non-linearity, time-varying parameters and unknown disturbance, so the tracking control of the robot is still challenging [1]. How to ameliorate the position tracking performance of the robot arms so that it has both excellent dynamic property and steady-state performance has become the research target of many scholars. Most of them design controller based on signal transform. A robust manipulator controller based on nonlinear observer is proposed in [2]. In [3], a robust control method based on position-torque conversion is proposed, which is expected to improve the position control accuracy by directly controlling the mechanical arm drive motor. Some scholars design the controller based on the viewpoint of energy transformation, whose control goal is to optimize the energy of the whole system. The idea of designing manipulator position controller based on PBC control method has a long history [4]. Some scholars also used the IDA-PBC control method to design a flexible articular manipulator to overcome the chattering problem of the manipulator and achieved good results [5]. In recent years, coordinated control strategy has attracted much attention due to its ability to give consideration to both dynamic performance and steady-state performance, and many achievements have been made [6, 7]. The (PMSM) has the advantages of energy © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 278–286, 2020. https://doi.org/10.1007/978-981-32-9050-1_32

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efficient and small torque ripple, so the permanent magnet synchronous motor is selected as the servo drive of the robot arm [8]. In order to give consideration to the dynamic and static performance of the manipulator position control at the same time, this paper proposes the control strategy of coordinating the reverse step synovial control and PCH control, and designs the smooth switching function based on the joint position error. Simlink simulation proves the effectiveness and advantages of the algorithm.

2 Induction Motor Model The manipulator dynamics can be indicated as DðqÞ€q þ Cðq; q_ Þq_ þ GðqÞ ¼ sm  Rf q_

ð1Þ

where sm is the control torque; q is the vector of joint positions, M ðqÞ 2 Rnn is the inertial matrix, C ðq; q_ Þ 2 Rnn is the Coriolis force and centripetal force matrix, GðqÞ 2 Rn is the gravity torque vector; Rf 2 Rn is the Frictional coefficient matrix. The Kinetic function of PMSM can be expressed as


h_ ¼ x Jm x_ þ Rm x ¼ s  sL

ð2Þ

where h is the motor position vector; x is the motor velocity vector; Jm is the motor rotational inertia matrix; Rm is the motor frictional coefficient matrix; sL is the load torque; s is the electromagnetic torque. For rigid manipulator, assuming that the motion is under ideal conditions. The relationship between the rotary axis of the servo motor and the corresponding joint axis can be expressed as. sm _ x_ ¼ k€ h ¼ kq; x ¼ h_ ¼ k q; q; sL ¼ k

ð3Þ

where k is the Reduction ratio. From (1), (2) and (3), we can derive generalized dynamics of n-joint manipulator can be described by ~ ðq; q_ Þq_ þ G ~ ð qÞ ¼ s ~ ðqÞ€q þ C D

ð4Þ

~ qÞ ~ ~ _ ¼ k1 Cðq; qÞ þ k1 Rf þ kRm , GðqÞ where DðqÞ ¼ k 1 DðqÞ þ kJm , Cðq; ¼ k1 GðqÞ. Through (4), the control input of the manipulator position control is equal to the electromagnetic torque of the servo motor.

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Fig. 1. The system diagram of the hybrid control system based on sign control and energy control

3 Controller Design The system diagram of the hybrid control system is as follows Fig. 1 3.1

Design of Signal Controller

Modified Backstepping Sliding Model Control _ New we rewrite the dynamics model (4) to Let us defined x1 ¼ q and x2 ¼ q. 8
~yij max Yj is expected to be large > > 1  i  m;1  k  hij > > n o < sðk Þ ~yij Yj is expected to be small ¼ 1  i  min m;1  k  hij > n o > > sðk Þ > > ~yij Yj is expected to the target : one of the

ð6Þ

1  i  m;1  k  hij

The vector formed by the optimal ideal value of each response  þ þ is defined
as the þ þ positive ideal solution of the multi-response process: Y ¼ y1 ; y2 ;


; yn . After determining the positive ideal solution of the response, the hesitant distance is calculated, and the extent of the hesitant fuzzy element ~yij ðj ¼ 1; 2;


; nÞ to its ideal solution can be easily measured. Formula (7) is the definition of hesitant distance: ffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2  2  2

1  l m u þ þ þ ~cij  yj dij ¼ þ ~cij  yj þ ~ cij  yj 3

ð7Þ

The smaller hesitant distance is, the closer process response corresponding to the hesitant fuzzy element to its ideal solution is. Since it’s common to measure the good and bad of things with the big characteristic, the similarity is defined according to the hesitant fuzzy distance in formula (8): Sij ¼ 1  dij ; ði ¼ 1; 2;


; n; j ¼ 1; 2;


; mÞ

ð8Þ

3 Fuzzy Logic Inference and Robust Optimization Design As mentioned above, the traditional method usually obtains the comprehensive quality indicator via weight setting, and then it will optimize the parameter. However, in practical applications, the weight is sometimes difficult to obtain, and the weight setting cannot provide enough information to describe the relationship among the responses,

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which will affect the result of parameter optimization. Fuzzy logic inference algorithm is introduced to simplify multi-response system. The comprehensive quality characteristic index–Fuzzy reasoning grade (FRG) will replace multi-response. There are some characteristics of fuzzy inference: the process of fuzzy inference can avoided weight setting firstly. Then it can fully consider the expert opinions. And it can also effectively utilize the mechanism relationship of the responses. (1) Ambiguity statements and fuzzy rules: Similarities are the inputs of fuzzy inference system, and FRG is the output. In the fuzzy inference system, inputs and output are firstly blurred. According to the actual situation, multiple similarities (denoted as) ðA; B


Þ and FRG (denoted as) Oð xÞ are divided into multiple linguistic variables, such as very large, large, medium, small, very small, etc. These variables are recorded as: A1 ; A2 ;


Am ; B1 ; B2 ;


; Bn ;


; C1 ; C2 ;


; Cq . The setting of fuzzy rules is mainly based on the fuzzy process mechanism, which is expressed in the form of ‘If


, then’. Take two inputs as an example: Rule 1: If d1 is A1 and d2 is B1 , then Oð xÞ is C1 else; Rule 2: If d1 is A2 and d2 is B2 , then Oð xÞ is C2 else; ... Rule P: If d1 is Am and d2 is Bn , then Oð xÞ is Cq . (2) The process of fuzzy logic inference: According to the linguistic variables of every similarity and FRG when setting the ~ i ði ¼ 1; fuzzy rule, the corresponding fuzzy subsets are respectively defined as A ~ q ðq ¼ 1; 2;


; QÞ. ~ j ðj ¼ 1; 2;


; nÞ and C 2;


; mÞ, B The membership functions of each fuzzy subset are recorded as: lA~ I ði ¼ 1; 2;


; mÞ; lB~ j ðj ¼ 1; 2;


; nÞ; lC~ q ðq ¼ 1; 2;


; QÞ Fuzzy inference is performed based on Mamdani’s fuzzy inference method [21]. (3) Clarify of output and robust optimization design: After rules’ setting, FRG need to be transformed from fuzzy set into a clear value by center of area method, and the system is simplified. Then the main effect analysis is employed to obtain the primary parameter combination. To expand the optimization range and improve the best parameter combination’s accuracy, the encrypted optimization interval is set near the primary optimal parameters by BP neural network.

4 Robust Optimization of Thermal Polymerization The production of thermal polymerization is a multi-input and multi-output process, in which there are three controllable factors, namely reaction time, reaction temperature and amount of catalyst. Every factor has two coded levels: −1 stands for low level,

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while +1 stands for high level. The conversion of polymer and the thermal activity are two responses in the process. According to the orthogonal theory, a L8(23) full factorial design is obtained. Every experiment under the same condition will repeat 4 times. The results can be seen in paper [20]. (1) Establishment of hesitant fuzzy decision matrix: After processing the experimental data of response values with formula (1)–(5), the hesitant fuzzy decision matrix which shows in Table 3 can be obtained. Table 3. Hesitant fuzzy decision matrix Run order x1 1 2 .. . 8

−1 1 .. . 1

x2 −1 −1 .. . 1

x3 −1 −1 .. . 1

~yi1

~ yi2

~cli1

~cm i1

~cui1

~ cli2

~ cm i2

~yui2

0.774 0.228 .. . 0.313

0.795 0.247 .. . 0.435

0.817 0.266 .. . 0.556

0.271 0.220 .. . 0.509

0.298 0.263 .. . 0.754

0.324 0.306 .. . 1.000

(2) Similarity calculation: According to the production requirements [20], the ideal values of y1 and y2 are 103 and 73, their normalized values are separately recorded as y+1 = 0.404 and y+2 = 0.661. The hesitant distance can be converted to similarity by formula (8). The calculation is shown in Table 4: (3) Fuzzy logic inference: The two values of similarity in Table 4 are inputs of the fuzzy system, and the FRG is the output, which represents the overall quality of the two responses Table 4. Hesitation distance, similarity and FRG Run order x1 x2 x3 1 −1 −1 −1 2 1 −1 − 1 .. .. .. ... . . . 8 1 1 1

d1 0.392 0.158 .. . 0.104

d2 0.364 0.400 .. . 0.221

S1 0.608 0.842 .. . 0.896

S2 0.636 0.6 .. . 0.779

FRG 0.714 0.775 .. . 0.775

In the fuzzy interface of MATLAB, S1 and S2 are taken as inputs of system, and FRG is the output. The two inputs’ domain is divided into five levels: very low, low, medium, high, very high; while the FRG’s domain is divided into six levels: level 1, level 2, level 3, level 4, level 5, Level 6. The membership function of every fuzzy subset is triangle membership function (Triangle MF), as shown in Fig. 1. The fuzzy inference rules shown in Table 5 are set according to the actual situation.

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Table 5. Fuzzy rules for thermal polymerization process Rule 1 2 .. . 13

If S1 is very low very low .. . medium

And S2 is very low low .. . medium

Then FRG is Rule Level1 14 Level1 15 .. .. . . Level4 26

If S1 is medium medium .. . None

And S2 is high very high .. . None

Then FRG is Level4 Level5 .. . None

Depending on the fuzzy rules, the FRG is performed by the center of area method. It’s clear to know the process of fuzzy logic reasoning in Fig. 2. There are three columns in the process viewer. The left two columns are the membership degrees of S1 and S2 , and the third column is the membership degree of FRG. When inputting any set of S1 and S2 , there will be a FRG accordingly. The outcomes are in Table 4.

Fig. 1. The membership function of S1

Fig. 2. Fuzzy reasoning process viewer

(3) Parameter optimization design: The main effect analysis selects the optimal level of all the controllable variables from the existing data. Input the data of controllable factors and FRG into the command interface of MINITAB, the optimal parameter combination will appear: The best level of x1 ; x2 and x3 are 1, 1 and −1, and the result will be regarded as preliminary parameter result. Because the main effect analysis can only select the optimal parameter combination from the two given levels of the controllable factors. The quality forecasting model of thermal polymerization process need to be established by BP neural network, which can further expand the optimization range.

Fig. 3. Training mean square error curve

Firstly, the data in Table 4 is used as the training sample for constructing the neural network: x1 ; x2 and x3 are taken as inputs, and the FRG value is the expected output. And the quality forecasting model makes up of only one hidden layer. The error curve of the training process is shown in Fig. 3, and the fitting degree of the model reaches 99.796%. Finally, the levels of best parameter combination ðx1 ; x2 ; x3 Þ is 1,1

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and −0.8, and the forecasting value of FRG increases by 0.07. Use the BP neural network forecasting model to predict the FRG values of the best parameter combination obtained by main effect analysis and BP forecasting mode separately, we can find that: the mean of FRG from BP forecasting mode basically agrees with than another group, while the SNR of FRG from BP mode is higher than another group. It verifies that the parameter optimization using BP forecasting mode has good effect and the robustness of the comprehensive quality index improves obviously.

5 Conclusion The results of proposed method are compared with the results of previous studies [22– 24], which are shown in Table 6. From Table 6, it can be seen that parameter optimization method in this paper is effective and practicable. Furthermore, the optimal parameter combination (1, 1, −0.8) makes robustness of the comprehensive quality index (FRG) get further improved.

Table 6. Comparative study Researcher (study time) Pignatiello [22] Chiao and Hamada [23] Ko et al. [24] Main effect method of this paper Neural network method in this paper

x1 1 1 1 1 1

x2 1 1 1 1 1

x3 −1 −1 −1 −1 −0.8

It proposes the multi-response parameter optimization method based on hesitant fuzzy sets in this paper. Considering the uncertainty of the response value under the complex industry process, hesitant fuzzy matrix is used to optimize the parameters and enhance the robustness of the process, which avoid the loss of information caused by the traditional method. The comprehensive quality index obtained by fuzzy logic inference can fully extract the mechanism information among multiple responses, which does not need to set the weight of multi-response. On the basis of the main effect analyzing, the BP predictive mode is established for global optimization, and the optimal robust parameters is sought. The application of the example shows the proposed method has great effectiveness and good practicability. Acknowledgment. This is founded by the National Aerospace Science Foundation (No. 2017ZG55029) and the Science and Technology Project of Henan Science and Technology Department, China (No. 182102210107).

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References 1. Su YP, Xu LH (2013) Dimensionality reduction based on geometric projection for manyobjective optimization. Control Decis. 28(8):1173–1177 (in Chinese) 2. Xu J, He W, Chen YZ et al (2016) A study of optimizing functional response problems based on principal component analysis. Ind Eng 19(1):74–80 (in Chinese) 3. Ma YZ, Wang JJ, Su GJ (2012) Robust parameter design for dynamic multi-response system. Syst Eng—Theory Pract 32(8):1841–1849 (in Chinese) 4. Liu YM, Zhao LX (2015) A robust optimization model for multi-responses based on desirability function approach and the empirical study. Oper Res Manag Sci 4(8):83–91 (In Chinese) 5. Sharma V, Kumar V (2016) Multi-objective optimization of laser curve cutting of Aluminium metal matrix composites using desirability function approach. J Braz Soc Mech Sci Eng 38(4):1221–1238 6. Sun WH, De-Quan HE (2006) The fuzzy integrated assessment of information security from qualitative to quantitative. Syst Eng-Theory Pract 26(12):93–98 7. Yu JL, Huang HQ, Chen HG (2017) RSM-based parameter optimization of electrotyping free-standing diamond-nickel composite film. Surf Technol 46(5):83–87 (In Chinese) 8. Gu XG, Ma YZ, Liu J et al (2017) Robust parameter design for multivariate quality characteristics based on process capability index with individual observations. Syst Eng Electron 39(2):362–368 (In Chinese) 9. Wang KT, Li F, Zhang XH et al (2015) Study on process parameters optimization based on error compensation mode. Control Eng 22(02):262–269 (In Chinese) 10. Zhao YP, Zhao GL, Wen YD et al (2018) Parameter optimization of feed forward neural networks based on variable projection approach. Control Eng 23(02):309–312 (In Chinese) 11. Yao GL (2018) Analysis on optimization of double-response surface-satisfaction function of surface work-hardening for cold roll-beating spline. J Plast Eng 25(03):129–135 (In Chinese) 12. Yan W, He Z, Tian WM (2012) Complex products critical-to-quality characteristics identification based on IG. Ind Eng Manag 17(1):70–74+83 (In Chinese) 13. Salmasnia A, Bashiri M (2015) A new desirability function-based method for correlated multiple response optimization. J Adv Manuf Technol 76(5–8):1047–1062 14. Vining GG (1998) A compromise approach to multi-response optimization. J Qual Technol 30(4):309–313 15. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353 16. Atanassov K (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96 17. Lee HJ, Jin BP, Chen G (2001) Robust fuzzy control of nonlinear systems with parametric uncertainties. IEEE Trans Fuzzy Syst 9(2):369–379 18. Torra V (2010) Hesitant fuzzy sets. Int J Intell Syst 25(6):529–539 19. Farhadinia B (2014) A series of score functions for hesitant fuzzy sets. Inf Sci 277(20):102–110 20. Bashiri M, Hosseininezhad SJ (2012) Fuzzy development of multiple response optimization. Group Decis Negot 21(3):417–438 21. Mamdani EH (1976) Advances in the linguistic synthesis of fuzzy controllers. J Man-Mach Stud 8(6):669–678 22. Pignatiellojr J (1993) Strategies for robust multi-response quality engineering. IIE Trans 25 (3):5–15 23. Chiao C, Hamada M (2001) Analyzing experiments with correlated multiple responses. J Qual Technol 33:451–465 24. Ko YH, Kim KJ, Jun CH (2005) A new loss function-based method for multi-response optimization. J Qual Technol 50–59:37

Active Disturbance Rejection and Adaptive Backstepping Control for Induction Motor with Smooth Switching of Rotor Flux Fei Gong, Haisheng Yu(&), Jinpeng Yu, and Xudong Liu Qingdao University, College of Automation, Qingdao, China [email protected], [email protected]

Abstract. The adaptive backstepping and smooth switching control method of rotor flux is investigated in the paper to achieve precise speed tracking control and minimum loss of induction motor. When the uncertain load first appears in the recursive design, the adaptive law of the load is designed, and the unknown load is estimated by novel adaptive load torque observer. By introducing the switching term into the ESO of ADRC, the current loop can converge rapidly to the desired current. And the smooth switching control method among the rated and the minimum rotor flux has been studied, which achieving the minimum loss of induction motor. The results demonstrate that the novel adaptive load torque observer acquire quick tracking of unknown load torque, and the smooth switching control method saves the loss of induction motor when it is in stable state. Keywords: Adaptive load torque observer
Novel ESO Minimum rotor flux
Smooth switching control method




1 Introduction In the actual operation of motor, the uncertain load disturbance will have a great influence on the function of control system, which cannot meet the goal of precise control. The vector control is widely used in IM speed control system because it is easy to implement and does not depend on accurate motor model [1, 2]. However, the dynamic response of vector control is slow, and the same PI parameters cannot meet the demands of various speeds and loads. The sliding mode variable structure control (SMVSC) has chattering in the system due to its own structural characteristics [3, 4]. Hamilton control can save the energy of the system with fewer parameters in controller [5, 6], and can follow the expected speed steadily when the parameters of the motor and load disturbance change, but the response speed of the system is slow. Jingqing Han proposed active disturbance rejection control (ADRC) [7], which has unique advantages in dealing with the sudden change of speed, load disturbance and time-varying parameters in IM. In each design step, the backstepping control with Lyapunov function defined recursively becomes the most widely used control strategy

© Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 296–304, 2020. https://doi.org/10.1007/978-981-32-9050-1_34

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in motor control with excellent tracking performance [8]. Unlike previous adaptive backstepping control, which designs load adaptive law in the last recursive step, the new adaptive backstepping control strategy proposed in this paper designs load adaptive law when uncertain load first appears in the recursive design step, and obtains a new adaptive load torque observer, which realizes online accurate load estimation and ensures high performance speed tracking control of IM.

2 Mathematical Model and Control Principle of Induction Motor With x  is  kr as the state variable, the mathematical expression is given by the following equation in the rotor flux-oriented control ðkrq ¼ 0Þ [9]. 8 Rs L2r þ Rr L2m disd Lm Rr 1 > isd þ np xisq þ LLrmkRrdr i2sq þ rL 2 2 krd þ rL usd > s s Lr s Lr > dt ¼  rL > 2 2 < disq Lm Rr isq R s Lr þ R r Lm Lm x r 1 ¼  i  n xi  i  k þ 2 sq p sd sd rd rLs usq dt Lr krd rLs Lr rLs Lr > dkrd Lm Rr Rr > > dt ¼ Lr isd  Lr krd > : dx ssL dt ¼ J

ð1Þ

 Where r ¼ 1  L2m ðLr Ls Þ is the leakage coefficient of motor. Ls , Lr , Rs and Rr represent the inductance and resistance of rotor and stator, respectively. isd , isq , usd and usq mean the components of stator current and voltage in d  q axes. np , Lm , krd , s, sL and x denote the pole pairs, mutual inductances, rotor flux, electromagnetic torque, load torque and speed, respectively. The block diagram of the novel adaptive backstepping, ADRC and smooth switching method of rotor flux for speed control system of induction motor has been displayed in Fig. 1. The speed and rotor flux loop of induction motor speed drive system are designed with backstepping controller. When the uncertain load first appears, the load adaptive law is designed to realize the accurate online estimation of the uncertain load. In the design of the current loop controller, the switching term is introduced into the extended state observer of the ADRC to realize the fast convergence of the controller in finite time. Aiming at the loss of IM, the relationship between the minimum rotor flux and loss has been established by analyzing the power and loss of induction motor. Finally, a smooth switching function is applied to coordinate the rated with the minimum rotor flux, which reduces the loss of induction motor in steady state and achieves the efficiency optimization of the induction motor.

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Fig. 1. The block diagram of adaptive backstepping and smooth switching method in IM drive system.

3 Design of Controller 3.1

Rotor Flux Observer

The Eq. (1) shows that the rotor flux is unknown, and an observer would be needed to estimate it [10]. The observer can be designed to estimate it by stator current isd of the rotor flux formula. _ k^rd ¼ ðLm Rr isd Þ=Lr  ðRr ^krd Þ=Lr

ð2Þ

The rotor flux error is expressed as ek ¼ krd  ^krd , and the differential of it can be represented as e_ k ¼ Rr ek =Lr . Define the Lyapunov function is V1 ¼ e2k =2, and its derivative is V_ 1 ¼ ek e_ k ¼ ðRr e2k Þ=Lr  0

ð3Þ

V_ 1 ¼ 0 if and only if ek ¼ 0, the rotor flux observer is asymptotically stable. 3.2

Adaptive Backstepping Controller

The speed error and rotor flux error are defined as ex ¼ x  x, ek ¼ krd  ^ krd , and their first derivatives are shown as _ _ e_ k ¼ k_ rd  ^ e_ x ¼ x_   x; krd

ð4Þ

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The adaptive backstepping control designs the load torque adaptive law when it first appears, and the load adaptive law designed realizes the online accurate estimation of the uncertain load torque. Choose the Lyapunov function V2 ¼ ðe2x þ e2k þ c1~s2L Þ=2

ð5Þ

Where c is a positive constant. ~sL ¼ sL  ^sL , ~sL and ^sL represent the load torque error and the estimated of the induction motor, respectively. The first order differential of V2 can be written as _ _ þ ek ðk_ rd  ^ V_ 2 ¼ ex e_ x þ ek e_ k þ c1~sL~s_ L ¼ex ðx_   xÞ krd Þ  c1~sL^s_ L

ð6Þ

According to the principle of backstepping control strategy, the virtual control current isq and the current error eiq are defined as isq ¼ðJLr x_   JLr 1Þ=np Lm ^krd ; eiq ¼ isq  isq

ð7Þ

Combining (1) and (7), (6) can be expressed as np Lm ^krd ^sL Lm Rr Rr ex V_ 2 ¼ krd Þ þ ~sL ð  c1^s_ L Þ ex eiq þ ex ð1 þ Þ þ ek ðk_ rd  isd þ ^ JLr J L L J ffl{zfflfflfflfflfflfflfflfflffl} r r |fflfflfflffl{zfflfflfflffl} |fflfflfflfflfflfflfflffl |fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl} k1 ex

k2 ek

k3 ~sL

ð8Þ Where ki [ 0ði ¼ 1; 2; 3Þ. Equation (8) shows that the stability function 1, virtual control current isq and load torque adaptive law can be computed as 1 ¼ k1 ex 

^sL  Lr _  Rr ex ;i ¼ ðk þ ^krd þ k2 ek Þ; ^s_ L ¼ ck3~sL þ c J sd Lm Rr rd Lr J

ð9Þ

Therefore, (8) can be reduced as V_ 2 ¼ðnp Lm ^krd ex eiq Þ=JLr  k1 e2x  k2 e2k  k3~s2L

ð10Þ

According to (1), the load torque of induction motor can be expressed as sL ¼ ðisq np Lm ^krd isq Þ=Lr  J x_

ð11Þ

Substituting (11) into (9), we can get the following equation. ^s_ L ¼ ck3 J x_ þ ðcex Þ=J þ ck3 ½ðnp Lm ^ krd isq Þ=Lr  ^sL 

ð12Þ

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Where s_ L;int ¼ ðcex Þ=J þ ck3 ½ðnp Lm ^krd isq Þ=Lr  ^sL . By integrating (12), the load torque observer can be expressed as Z ^sL ¼ ck3 J

xðtÞ xð0Þ

dx þ sL;int

ð13Þ

Combining the (4), (7) and (9), it can be obtained the following equation. e_ x ¼ ðnp Lm ^krd eiq Þ=ðJLr Þ  k1 ex þ ~sL =J krd Þ isq ¼½JLr k1 ðx  xÞ=ðnp Lm ^krd Þ þ ðLr ^sL Þ=ðnp Lm ^krd Þ þ ðJLr x_  Þ=ðnp Lm ^

ð14Þ ð15Þ

Putting (14) and (15) into (10), V_ 2 is shown as V_ 2 ¼  k1 e2x  k2 e2k  k3~s2L \0

3.3

ð16Þ

Novel ESO Design

By introducing the switching term into the ESO of ADRC, the current loop can converge rapidly to the desired current. From the Eq. (1), the d axes stator current can be expressed as _isd ¼ ad ðtÞ þ usd =ðrLs Þ

ð17Þ

R L2 þ R L2

Lm Rr s m r Where ad ðtÞ ¼  s rL isd þ np xisq þ LLrmkRrdr i2sq þ rL 2 2 krd are the lumped disturs Lr s Lr bance of d axes current loop. According to [10], The ESO with switching term is given by

(

^_isd ¼ usd0 =ðrLs Þ þ ^ad ðtÞ þ 2q1 ðjed jm1 signðed Þ þ jed jn1 signðed ÞÞ þ k4 sgmf ðed Þ ð18Þ ^a_ d ðtÞ ¼ q2 ðjed jm2 signðed Þ þ jed jn2 signðed ÞÞ þ k5 sgmf ðed Þ 1

Where ed ¼ isd  ^isd , 1\q1 \ þ 1, 0:5\m1 \1, m2 ¼ 2m1  1.  sgmðed Þ ¼

2=ð1 þ expeed Þ  1; jed j  d signðed Þ; jed j [ d

ð19Þ

where e is a constant, d is a constant of error. By introducing the concept of feedforward compensation into the $d$-axes current loop, the output is designed as usd ¼ usd0 þ rLs ^ad ðtÞ; usd0 ¼ kd ðisd  isd Þ where kd is a proportional constant.

ð20Þ

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In the same way, the ESO with switching term is given by (

    ^_isq ¼ usq0 =ðrLs Þ þ ^aq ðtÞ þ 2q2 ðeq m3 signðeq Þ þ eq n3 signðeq ÞÞ þ k6 sgmf ðeq Þ     ð21Þ m n ^a_ q ðtÞ ¼ q2 ðeq  4 signðeq Þ þ eq  4 signðeq ÞÞ þ k7 sgmf ðeq Þ 2

Where eq ¼ isq  ^isq , 1\q2 \ þ 1, 0:5\m3 \1. ^ aq ðtÞ is the estimated value of aq ðtÞ ¼ tisq  np xisd  ðLm Rr isq isd Þ=ðkrd Lr Þ  ðLm xr krd Þ=ðrLs Lr Þ: The composite controller of q axes current loop is given by usq ¼ usq0 þ ^aq ðtÞrLs ; usq0 ¼ kq ðisq  isq Þ

ð22Þ

where kq is a proportional constant. 3.4

Smooth Switching of Rotor Flux

When induction motor is in stable state, the mathematical model of it is written as usd ¼ Rs isd  ðnp x þ

Rr Lm Rr Lm isq Þisq Lr ; usq ¼ Rs isq þ ðnp x þ isq Þisd Ls Lr krd Lr krd

ð23Þ

Where Lr ¼ ðLs Lr  L2m Þ=Lr . The electromagnetic torque can be derived as s ¼ ðnp L2m isd isq Þ=Lr . Input power of induction motor can be computed as Pin ¼ usd isd þ usq isq

ð24Þ

Substituting (23) into (24), it can be obviously get that Pin ¼ ðRs k2rd Þ=L2m þ ðRs s2 L2r Þ=ðn2p L2m k2rd Þ þ sx þ ðs2 Rr Þ=ðn2p k2rd Þ

ð25Þ

The output power of IM is expressed as Pout ¼ sx. In term of the loss of IM, we have Ploss ¼ Pin  Pout ¼ Rs ð

1 k2rd s2 L2r s2 Rr Rr L2m 14 þ Þþ ; krdo ¼ ðs=np Þ2 ðL2r þ Þ 2 2 2 Lm Rs n2p L2m krd n2p krd

ð26Þ

In conventional IM control system, a rated rotor flux is generally employed in reducing system response time. However, when the motor is running at a stable state, the rated rotor flux will cause the loss to be large, failing to achieve the goal of energy saving. In this paper, the smooth switching function is designed to coordinate the rated with the minimum rotor flux. And when the speed or load torque varies immediately, the smooth switching method is restarted to realize the coordination control effect of the whole drive system.

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The smooth switching controller of rotor flux can be chosen as m

m

krd ¼ krd expðtta Þ =l þ ð1  expðtta Þ =l Þkrdo

ð27Þ

Where m, l are constants designed by the dynamic response of IM and ta is the time point at which speed immediately varies.

4 Simulation Results The novel adaptive backstepping and smooth switching of rotor flux is simulated and validated on MATLAB/Simulink, and contrasted with vector control. The parameters of IM used in this paper are as follows: PN ¼ 2:2 KW, xN ¼ 183 rad=s, krdN ¼ 0:8 Wb, IN ¼ 8 A, UN ¼ 220 V, Rs ¼ 0:687 X, Ls ¼ 0:084 H, Lr ¼ 0:0852 H, Lm ¼ 0:0813 H, np ¼ 2, J ¼ 0:03 kg
m2 . The parameters of controllers: k1 ¼ 100, k2 ¼ 5000, k3 ¼ 35000, c ¼ 0:025, m1 ¼ m3 ¼ 0:8, q1 ¼ q2 ¼ 10, kd ¼ kq ¼ 500.

Fig. 2. The curve of speed tracking.

Fig. 4. The loss waveforms.

Fig. 3. The rotor flux waveforms.

Fig. 5. Load torque observation curve.

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The initial load torque of IM is sL ¼ 2 N
m, and it changes from 2 N
m to 6 N
m. The rated rotor flux is 0:8 Wb, the given speed is 100 rad=s in the beginning simulation, and it turns to 150 rad=s. The control strategy designed in this paper has been compared with vector control. It can be seen from the speed curve of Fig. 2 that the control strategy designed in this paper has a faster speed response, can accurately track the set speed value, and quickly eliminates the impact of load disturbance. The system has superior performance on load disturbance attenuation. From the rotor flux observation curve and loss curve of Figs. 3 and 4, the smooth switching method can effectively reduce the loss of the IM during steady state operation and ensure the dynamic performance. According to the load torque estimation curve in Fig. 5, the novel adaptive load torque observer achieves accurate estimation of unknown load.

5 Conclusion In this paper, a novel adaptive backstepping control strategy has been designed in speed control system of IM, solving the issue of uncertain load. When the uncertain load first appears in the recursive design step, the adaptive law of the load is obtained to estimate the uncertain load accurately. The adaptive backstepping control algorithm proposed realizes high performance speed tracking control. The simulation results show that the observer achieves on-line accurate estimation of uncertain load torque. By introducing the switching term into the ESO of ADRC, the current loop can converge rapidly to the desired current. Compared with vector control method, the speed response is faster, and the effect of load disturbance on speed is eliminated quickly. And the coordination control strategy not only ensures the fast performance of IM, but also reduces the loss in stable state.

References 1. Kobayashi N, Wijaya FP, Kondo K et al (2016) Induction motor speed-sensorless vector control using mechanical simulator and disturbance torque compensation. IEEE Trans Ind Appl 52(3):2323–2331 2. Deng YH, Liang ZS, Xia PC et al (2019) Improved speed sensorless vector control algorithm of induction motor based on long cable. J Electr Eng Technol 14:219–229 3. Oliveira CMR, Aguiar ML, Monteiro, et al (2016) Vector control of induction motor using an integral sliding mode controller with anti-windup. J Control, Autom Electr Syst 27 (2):169–178 4. Almeida JM, Loukianov AG, Dominguez JR (2018) Robust sensorless observer-based adaptive sliding modes control of synchronous motors. J Franklin Inst 355(7):3221–3248 5. Yu HS, Yu JP, Liu J et al (2013) Nonlinear control of induction motors based on state error PCH and energy-shaping principle. Nonlinear Dyn 72(1–2):49–59 6. Yu HS, Yu JP, Liu J et al (2012) Energy-shaping and L2 gain disturbance attenuation control of induction motor. Int J Innovative Comput Inf Control IJICIC 8(7):2024–5011 7. Han JQ (2009) From PID to active disturbance rejection control. IEEE Trans Ind Electron 56 (3):900–906

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8. Yu J, Ma Y, Yu H et al (2017) Adaptive fuzzy dynamic surface control for induction motors with iron losses in electric vehicle drive systems via backstepping. Inf Sci 376:172–189 9. Sun W, Liu X, Gao J et al (2016) Zero stator current frequency operation of speed-sensorless induction motor drives using stator input voltage error for speed estimation. IEEE Trans Ind Electron 63(3):1490–1498 10. Xiong SF, Wang WH, Liu XD et al (2015) A novel extended state observer. ISA Trans 58:309–317

Super-Twisting and Nonsingular Terminal Sliding Mode Direct Torque Control of Induction Motor Drives Wenchao Lv and Haisheng Yu(&) Qingdao University, Qingdao 266071, China [email protected]

Abstract. This paper proposes a non-linear and robust Direct Torque Control (DTC) strategy for induction motor, which with Space Vector Pulse Width Modulation (SVPWM). In order to overcome the disadvantages of large torque and current ripple in traditional DTC of induction motor, Super-twisting (St) speed controller is designed to replace the traditional Proportional Integral (PI) controller. According to the induction motor’s mathematical model and Nonsingular Terminal Sliding Mode (NTSM) control theory, a robust sliding mode controller based on torque error and stator flux squared error is designed. For the sake of estimating the load torque applied to motor precisely, as well eliminating uncertainties of the system, an observer based on Super-twisting algorithm is designed. Super-twisting stator flux observer is applied to improve the observation accuracy. Simulation results indicate that this control strategy can reduce torque and current ripple effectively, and has a strong inhibition effect on external disturbance, with good dynamic and steady performance. Keywords: Induction motor DTC
Super-twisting Nonsingular Terminal Sliding Mode
SVPWM




1 Introduction With the rapid development of modern industry and continuous progress of automation, AC speed regulation system has developed rapidly because of its good steady and dynamic performance. DTC has been widely used because of its advantages over Field Oriented Control (FOC). Traditional DTC has the advantages of simple structure, quick response, strong robustness to the variation of internal parameters and external interference of the system, but at the same time, there are also problems such as excessive torque, current and flux ripple, besides the switching frequency of inverter is not constant, and it’s hard to accurately control the system at low speed state [1, 2]. To overcome the drawbacks, several control strategies were utilized, such as using sensorless control, Active Disturbance Rejection Control and many robust nonlinear control techniques [3–5]. Energy-shaping and PCH strategy has been utilized extensively on account of real-time energy optimization [6]. The complex computing process of fuzzy logic as well as artificial neural network limits their wide application [7, 8].

© Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 305–312, 2020. https://doi.org/10.1007/978-981-32-9050-1_35

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Sliding Mode (SM) control is extensively used for uncertain systems, for it possesses the advantages of fast response, strong robustness, and is not affected by external disturbances and internal parameter changes [9]. The SM controller has been used to replaces the hysteresis controller to improve the response speed and effectively reduce the current and torque ripple. Super - twisting (St, similarly hereinafter) is a high order algorithm of sliding mode, it generates a continuous control law to maintain fast response and high robustness, at the same time, can reduce the chattering phenomenon [10]. Nonsingular Terminal Sliding Mode can achieve convergence in finite time in theory and have better anti-interference performance in engineering. In this paper, the St speed controller is going to be designed to generate the torque reference, the torque and flux linkage controllers based on the Nonsingular Terminal Sliding Mode generate reference voltage vector. Then, an observer on the basis of Super-twisting algorithm is going to be designed to estimate the load torque applied to motor and eliminate uncertainties. For the sake of improving the induction motor stator flux observation accuracy, a Super-twisting stator flux observer is designed.

2 Induction Motor Model In the stationary reference, the stator currents as well as stator flux are taken as the state variables, and the induction motor model should be expressed as: 8 _isa ¼ aisa þ bksa þ hksb þ gusa  xr isb > >

k_ ¼ Rs isa þ usa > : _ sa ksb ¼ Rs isb þ usb

ð1Þ

a ¼ Rs =ðrLs Þ þ Rr =ðrLr Þ; b ¼ Rr =ðrLs Lr Þ; h ¼ xr =ðrLs Þ; g ¼ 1=ðrLs Þ: The electromagnetic torque can be described as s ¼ np ðksa isb  ksb isa Þ, where, isa and isb are the value of stator current, usa and usb are stator voltage components.ksa , ksb and Rs are stator flux components and stator resistance. Rr represents rotor resistance, Ls and Lr are stator inductance and rotor inductance, respectively. Lm is mutual inductance between rotor and stator, where np represents the number of pole-pairs, s means electromagnetic torque. xrm is mechanical angular speed, as well xr ¼ np xrm repre sents electrical angular speed of rotor. r ¼ 1  L2m ðLr Ls Þ represents leakage coefficient. The DTC of induction motor based on Super-twisting and Nonsingular Terminal Sliding Mode system block diagram is shown in Fig. 1.

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Fig. 1. DTC based on St and NTSM

3 Direct Torque Control System Design 3.1

Super-Twisting Speed Controller

The induction motor’s mechanical equation is described as x_ rm ¼ ðs  sL Þ= Jm  ðBxrm Þ=Jm . Speed observation error will be defined as sx ¼ xrm  xrm , its derivative s_ x ¼ x_ rm  x_ rm . Substitute mechanical equation into the equation of speed observation error, and we will get s_ x ¼ x_ rm  ðs  sL  Bxrm Þ=Jm , Jm is inertia moment, sL is load torque, and B represents coefficient of friction. Based on the theory of conventional sliding mode control, we can obtain s ¼ seq þ sn , seq and sn represent the equivalent and nolinear control part, respectively. seq is defined when s_ x ¼ 0, seq ¼ ^sL þ Bxrm , ^sL represents the estimated value of load torque, and the nolinear part of control law is related to the sign of the speed observation error, as sn ¼ Kx sgnðsx Þ. The St speed controller replaces the conventional nolinear part, it can be defined as u_ st ¼ u1 þ kx jsx j1=2 sgnðsx Þ, u_ 1 ¼ bx sgnðsx Þ. From the above, the generated reference torque by the super twisting controller is given by s ¼ seq þ ust . 3.2

Torque and Flux Nonsingular Terminal Sliding Mode Controllers

The SM controller is proposed to replace the hysteresis controller to reduce the torque and current ripple. Torque and flux linkage errors are defined as es ¼ s  s, 2  2 ek ¼ k2 s  ks , s , ks is the expected value of torque and squared flux, respectively. s,

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k2s is the actual value of torque and squared flux, respectively. The derivative of torque error can be obtained:   R r np xrm isa ksa þ isb ksb þ rL þ r  e_ s ¼ _s ¼ np 4 n x  p rm k2sa þ k2sb þ us rLs 2

Rs rLs



 3 isa ksb  isb ksa  5

ð2Þ

  In Eq. (2), us ¼ isb  ksb =ðrLs Þ usa þ ðksa =ðrLs Þ  isa Þusb . In order to improve the response speed, as well reduce the torque ripple, a non-singular terminal sliding mode surface is proposed as ss ¼ es þ b1 e_ ps 1 =q1 . For the sake of weakening the chattering problem of traditional sliding mode control, the torque control law consists of equivalent control term useq and nonlinear switching term usn :   R r þ np xrm isa ksa þ isb ksb þ rL r  ¼ 4 n x  2 2 p rm ksa þ ksb rLs 2

useq

Rs rLs



 3 isa ksb  isb ksa  5

ð3Þ

 Rt  usn ¼ 0 ðq1 =ðb1 p1 ÞÞ_eðs2p1 =q1 Þ þ ks sgnðss Þ ds, ks [ 0. Take the positive definite Lyapunov function as V_ s ¼ 12 s2s . The derivative of this equation is p1 V_ s ¼ ss s_ s ¼ b1 e_ sðp1 =q1 1Þ ðks jss jÞ\0 q1

ð4Þ

From the above equation, the system will reach and maintain the non-singular terminal sliding surface in a limited time, can also converge in finite time. The torque nonsingular terminal sliding mode controller is asymptotically stable. The derivative of flux linkage error can be obtained as e_ k ¼ k_ 2s ¼ 2Rs isa ksa þ 2Rs isb ksb  uk . In the last equation, uk ¼ 2ksa usa þ 2ksb usb . In order to improve the response speed, as well weaken the steady state flux pulsation, the non-singular terminal sliding mode p =q surface is proposed as sk ¼ ek þ b2 e_ k2 2 . As to weaken the chattering problem of traditional SM control, the flux control law can be composed of equivalent control term Rt ukeq ¼ 2Rs isa ksa þ 2Rs isb ksb and nonlinear switching term ukn ¼ 0 ½ðq2 =ðb2 p2 ÞÞ ð2p =q Þ

e_ k 2 2 þ kk sgnðsk Þdk, kk [ 0. Take the positive definite Lyapunov function as V_ k ¼ 12 s2k . The derivative of this equation can be obtained: p2 ðp =q 1Þ V_ k ¼ sk s_ k ¼ b2 e_ k 2 2 ðkk jsk jÞ\0 q2

ð5Þ

From the above equation, the system will reach and maintain the non-singular terminal sliding surface in a limited time, can also converge in finite time. The torque nonsingular terminal sliding mode controller is asymptotically stable. Therefore,

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according to equations presented in this paper, it can be obtained us and uk , combined with this, the output of the controller is: U¼

3.3

ksa ðisa rL Þu þ 2ksb us s k

k2 2ðisa ksa þ isb ksb rLss Þ

k

ðisb rLsbs Þuk 2ksa us

T

k2 2ðisa ksa þ isb ksb rLss Þ

ð6Þ

Super-Twisting Observer

When the load torque is unknown variable, speed observation error is introduced to calibrate the load torque observer. The observation error of speed is taken as the sliding ^ rm , and according to the Super-twisting control law, mode variable, as st ¼ xrm  x then we can design the load torque observer as ^sL ¼ ^sL1 þ ksl jst j1=2 sgnðst Þ, ^s_ L1 ¼ bsl sgnðst Þ. The current observation error is introduced for correction, as sia ¼ isa  ^isa , sib ¼ isb  ^isb . According to the Supertwisting control law, the stator flux observer can be constructed as: 8 ^_isa ¼ a^isa þ b^ksa þ h^ksb þ gusa  xr isb þ la1 jsia j1=2 sgnðsia Þ > > > > < ^_ isb ¼ a^isb þ b^ksb  h^ksa þ gusb þ xr isa þ lb1 jsib j1=2 sgnðsib Þ _ > > ^ksa ¼ Rs^isa þ usa þ la2 sgnðsia Þ > > : ^_ ksb ¼ Rs^isb þ usb þ lb2 sgnðsib Þ

ð7Þ

4 Simulation Results MATLAB/Simulink was used for simulation research, and the asynchronous motor control system was built according to Fig. 1. The main parameters are: rated power Pn ¼ 1:5 KW, rated voltage Un ¼ 220 V, rated current In ¼ 6 A, rated frequency fn ¼ 52 Hz, rated speed nN ¼ 1500 r=min, ks ¼ 0:8 Wb, Rs ¼ 0:96419 X, Rr ¼ 0:93766 X, np ¼ 2, Ls ¼ 6:08925 mH, Lr ¼ 6:43858 mH, Lm ¼ 112:23209 mH, Jm ¼ 0:0038 kg m2 , B ¼ 0:001 N m s. The induction motor’s speed is changed from 60 rad=s to 150 rad=s when t ¼ 0:9 s. Then, additional load torque disturbance 4 N  m is introduced to the system from 1 N  m when t ¼ 1:6 s. Compare the data in Fig. 2, the torque fluctuation range of DTC based on St and NSTM is much smaller compared with traditional DTC, indicates this control strategy can effectively reduce torque ripple.

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Fig. 2. Electromagnetic torque curve

The speed tracking curve as shown in Fig. 3. Through the comparison of the data from Fig. 3, DTC based on St and NSTM can track the speed quickly, and has small chattering phenomenon. When the load is suddenly increased, there is only a slight fluctuation and the speed can be quickly returned to the given value, which is superior to the traditional DTC.

Fig. 3. Rotor speed curve

Figure 4 shows the load torque observation curve. The introduction of load torque observer reduces the cost of the system and reduces the uncertainty of the control system. It can be observed from Fig. 4, the load torque observer based on Super twisting can achieve accurate observation of the load torque changes. It is obvious that the stator current of DTC based on St and NSTM doesn’t have big ripple when the speed increases or load torque suddenly changes (Fig. 5).

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Fig. 4. Load torque observation curve

Fig. 5. Stator current curve of DTC based on St and NTSM

5 Conclusion This paper designs speed controller on the basis of St algorithm, and the NTSM approach has been utilized to design torque and flux controllers. The observers based on St algorithm are introduced to improve the precision of induction motor control. Simulation results can show the control strategy presented by this paper not only lowers torque ripple and current ripple of the traditional DTC, but also can ensure the switching frequency of inverter being approximate constant, weaken the chattering phenomenon of the traditional sliding mode method. The control scheme shows strong robustness about the whole system. In conclusion, the designed control method has superior theoretical significance and application prospect. Acknowledgement. This paper is supported by the Natural Science Foundation of China (61573203).

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References 1. Kumar RH, Iqbal A, Lenin NC (2018) Review of recent advancements of direct torque control in induction motor drives – a decade of progress. IET Power Electron 11(1):1–15 2. Ryu JH, Lee KW, Lee JS (2006) A unified flux and torque control method for DTC-based induction-motor drives. IEEE Trans Power Electron 21(1):234–242 3. Zaky MS, Metwally MK, Azazi H et al (2018) A New Adaptive SMO for speed estimation of sensorless induction motor drives at zero and very low frequencies. IEEE Trans Ind Electron 65(9):6901–6911 4. Yin Z, Du C, Liu J et al (2018) Research on autodisturbance-rejection control of induction motors based on an ant colony optimization algorithm. IEEE Trans Ind Electron 65(4):3077– 3094 5. Ammar A, Bourek A, Benakcha A (2017) Nonlinear SVM-DTC for induction motor drive using input-output feedback linearization and high order sliding mode control. ISA Trans 67 (Complete):428–442 6. Yu H, Yu J, Liu J et al (2013) Nonlinear control of induction motors based on state error PCH and energy-shaping principle. Nonlinear Dyn 72(1–2):49–59 7. Zeb K, Uddin W, Haider A et al (2018) Robust speed regulation of indirect vector control induction motor using fuzzy logic controllers based on optimization algorithms. Electr Eng 100(2):787–802 8. Zidani Y, Zouggar S, Elbacha A (2018) Steady-state analysis and voltage control of the selfexcited induction generator using artificial neural network and an active filter. Iran J Sci Technol Trans Electr Eng 42(1):41–48 9. Park SC, Lee JM, Han SI (2018) Tracking error constrained terminal sliding mode control for ball-screw driven motion systems with state observer. Int J Precis Eng Manuf 19(3):359– 366 10. Utkin V (2018) Mechanical energy-based Lyapunov function design for twisting and supertwisting sliding mode control. IMA J Math Control Inf 32(4):675–688

Four Quadrant PMSM Drive System via Backstepping and Hamiltonian Control Guanglin Lv, Haisheng Yu(&), and Xudong Liu Qingdao University, Qingdao 266071, China [email protected]

Abstract. The DC bus voltage overshoot and poor tracking effect of motor speed are the common problems in the four quadrant permanent magnet synchronous motor drive system, a hybrid control strategy is designed. The control objectives are as follow: (i) the DC bus voltage can fast track the reference voltage; (ii) the power factor is close to one on the grid side; (iii) the motor runs in four quadrant and have a good speed tracking. The models of the grid-side and PMSM are established respectively. The controller is designed through the PCH system and backstepping. Simulation results show that the system’ s DC bus voltage has no overshoot and the motor speed tracking effect is better, the design scheme is viable. Keywords: PMSM


Four quadrant
Backstepping
PCH

1 Introduction Nowadays, the AC motor has been widely studied by the domestic and overseas scholars [1–3]. However, in the traditional four quadrant PMSM drive system, the grid converter used the diode or thyristor, it is difficult to solve the problems. Recently, the emerging back-to-back converter has more advantages which based on the IGBTs [4, 5]. Many control strategies has been studied to improve the operation performance of the four quadrant PMSM drive system. In paper [6], robust control has used to the four quadrant PMSM drive system which based on the AC/DC/AC converter, the DC Link voltage has fluctuated greatly when the load has changed. In paper [7], the adaptive control base on the model reference has presented, the DC bus voltage has a overshoot. In this paper, a control strategy based on the backstepping control and the PCH system have presented. On the grid side, backstepping and PCH control is adopted to the voltage and current loop, respectively. On the motor side, the backstepping controller is used to realize the speed tracking.

2 The Control Scheme of the PMSM Drive System The control scheme of the PMSM drive system is shown in Fig. 1. It is made up of grid side subsystem and motor side subsystem. © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 313–319, 2020. https://doi.org/10.1007/978-981-32-9050-1_36

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Fig. 1. The control scheme

3 Mathematical Model of PMSM Drive System The grid side subsystem is expressed as 8 < Ldigd =dt¼  Rigd þ xg Ligq  ugd þ egd Ldigq =dt ¼ Rigq  xg Ligd  ugq þ egq : Cdu =dt ¼ i  i ¼ l i þ l i  i dc L L gd gd gq gq

ð1Þ

Where, igd and igq are the d-q axes current; egd and egq are voltage in d-q axes; xg is  pffiffiffiffiffiffiffiffi T T the angular frequency; ½ egd egq  ¼ 3=2Em 0 ; lgd and lgq are duty ratio in d-q axes; udc denotes the DC bus voltage. The motor side subsystem is written as 8 < Ld dimd =dt ¼ umd  Rs imd þ np xm Lq imq Lq dimq =dt ¼ umq  Rs imq  np xm Ld imd  np xm U : Jm dxm =dt ¼ s  sL ¼ np ðLd  Lq Þimd imq þ np Uimq  sL

ð2Þ

Where, Lm ¼ Ld ¼ 0:0852 H, U¼ 0:175 Wb, np ¼ 4; Rs ¼ 2:875 X; xm is motor speed; s and sL denote electromagnetic torque and load torque, respectively.

4 The Controller of PMSM Drive System 4.1

The Controller of the Grid Side Subsystem

There are two control goals: (i) udc can fast track the reference voltage Vdc . (ii) the power factor is close to one. Based on the power conservatism principle, we can get ed igd þ eq igq ¼ udc idc ¼ udc ðC

dudc C du2dc þ iL Þ ¼ þ udc iL 2 dt dt

ð3Þ

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According to Eq. (1) and the Eq. (3), we get Cdu2dc =dt ¼ 2ed igd  2udc iL

ð4Þ

2 The e1 stands up as a virtual control signal, where e1 ¼ Vdc  u2dc , from (1, 2) we

get 2 e_ 1 ¼ V_ dc  u_ 2dc ¼  2ðed igd  udc iL Þ=C

ð5Þ

To get a stabilizing control law igd , Consider the Lyapunov function Vge ¼ e21 =2, so  igd ¼ðkg1 Ce1 2 þ udc iL Þ=ed

ð6Þ

The PCH model is described as 

  x_ g ¼ J g ðxg Þ  Rg ðxg Þ @Hg ðxg Þ=@xg þ gg ðxg Þug yg ¼ gTg ðxg Þ@Hg ðxg Þ=@xg

ð7Þ

Where, J g ðxg Þ ¼ J Tg ðxg Þ and Rg ðxg Þ ¼ RTg ðxg Þ  0 represent the interconnection and damping structure. The state, input variable are defined as follow xgi ¼ ½ xgi1

xgi2 T ¼ ½ Ligd

Ligq T ; ugi ¼ ½ ugi1

ugi2 T

ð8Þ

The Hamiltonian function is chosen to be Hg ðxg Þ ¼¼ ðx2gi1 þ x2gi2 Þ=2L, where D ¼diagf L L g. Substituting Eq. (2) into (7), with ugi1 ¼ ed  lgd udc , ugi2 ¼ eq  lgq udc , we get   0 xgi L Rgi J gi ðxgi Þ ¼ ; Rgi ðxgi Þ ¼ xgi L 0 0 

0 Rgi



 To make the current loop is asymptotically stable at equilibrium xgi0 ¼ Ligd we find u ¼ aðxÞ, we get   x_ gi ¼ J gid ðxgi Þ  Rgid ðxgi Þ @Hgid ðxgi Þ=@xgi

ð9Þ 0

T

,

ð10Þ

Where, Hgid ðxgi Þ¼Hgi ðxgi Þ þ Hgia ðxgi Þ is the expected Hamiltonian energy function, J gid ðxgi Þ ¼ J gi ðxgi Þ þ J gia ðxgi Þ, Rgid ðxgi Þ ¼ Rgi ðxgi Þ þ Rgia ðxgi Þ. Combined Eqs. (7) and (10), we get 

J gid ðxgi Þ  Rgid ðxgi Þ

 @Hgid @xgi

  @Hgi ðxgi Þ ¼ J gi ðxgi Þ  Rgi ðxgi Þ ðxgi Þ þ ggi ðxgi Þu ð11Þ @xgi

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Defined the expected Hamiltonian energy function Hgid ðxgi Þ ¼ Hgi ð~ xgi Þ, where ~xgi þ xgi0 ¼ xgi , so  @Hgid ðxgi Þ ¼ igd  igd @xgi

igq  igq



ð12Þ

At the same time, let  J gia ðxgi Þ ¼

0 Jgi1

  Jgi1 r ; Rgia ðxgi Þ ¼ gi1 0 0

0



rgi2

ð13Þ

Where, the interconnection and damping parameters are described by Jgi1 , rgi1 , rgi2 . Combined Eqs. (9), (11) and (13), we get the controller as follow 

ugd ¼ egd  Rigd  rgi1 ðigd  igd Þ  Jgi1 igq ugq ¼ egq þ rgi2 igq  Jgi1 ðigd  igd Þ  xgi Ligd

ð14Þ

Consider the Lyapunov function as Vgi ¼ Hgid ðxgi Þ, from the reference [8], we know the system (11) is asymptotically stable. So Vgri ¼Vge þ Vgi and V_ gri  0. The grid side subsystem is asymptotically stable. 4.2

The Controller of the Motor Side Subsystem

There are two control goals: (i) xm can fast track the reference value xm . (ii) the four quadrant operation. The e2 stands up as the speed tracking error, where e2 ¼ xm  xm , from Eq. (2) we get e_ 2 ¼ ðnp ½ðLd  Lq Þimd imq þ Uimq   sL Þ=Jm

ð15Þ

Select the Lyapunov function Vs1 ¼ e22 =2, let e_ 2 ¼ km1 e2 ðkm1 [ 0Þ, so V_ s1 ¼  km1 e22 \0. About surface permanent magnet synchronous motor, from Eq. (13) we get imq ¼ ðsL þ Jm km1 e2 Þ

ð16Þ

The e3 stands up as the current error in q axes, where e3 ¼ imq  imq , so e_ 3 ¼ ðRs imq þ np xm Lq imd þ np xm U  umq Þ=Lq

ð17Þ

In order to get a stabilizing control law umq , choose the Lyapunov function as V3 ¼ e23 =2, so

Four Quadrant PMSM Drive System via Backstepping and Hamiltonian Control

umq ¼ Rs imq þ np xm Lq imd þ np xm U þ km2 Lq e3 ; ðkm2 [ 0Þ

317

ð18Þ

Combined Eqs. (17) and (18), we get e_ 3 ¼ km1 e3 , so V_ 3 ¼ km1 e23 \0. Based on the imd ¼ 0 principle, The e4 stands up as the current error in d axes, where e4 ¼ imd  imd , so e_ 4 ¼ ðRs imd  umd  np xm Lq imq Þ=Ld

ð19Þ

In order to get the control law umd , choose the Lyapunov function V4 ¼ e24 =2 þ V3 , so umd ¼ Rs imd  np xm Ld imq þ km3 Ld e4 ðkm3 [ 0Þ

ð20Þ

We can get e_ 4 ¼ km3 e4 , thus V_ 4 ¼ km3 e24 þ V_ 3 \0. Therefore, the motor side subsystem is asymptotically stable.

5 Simulation Result Grid side parameter: R ¼ 1 X, Em ¼ 80 V, xg ¼ 50 Hz, C ¼ 2200 lF, kg1 ¼ 55, Jgi1 ¼  3:6, rgi1 ¼0:01, rgi2 ¼0:45. Setting the DC bus voltage Vdc ¼200 V. Motor side parameter: km1 ¼ 9000, km2 ¼ 80, km3 ¼ 1750. The DC bus voltage curve was shown by Fig. 2. The voltage not only can quickly reach the set value and but also have no overshoot. Although the voltage has fluctuation when the load or the speed has changed, the DC bus voltage can stay stable.

Fig. 2. The DC bus voltage curve

Fig. 3. The current curve in d-q axes

The current in d-q axes was shown by Fig. 3. In the process of system operation, igq is closed to one, the unit power factor has realized. The motor angle speed curve was shown by Fig. 4. The Fig. 5 shows the electromagnetic and load torque curve. The motor angle speed can quickly reach the set value and have a good tracking performance. At 0.2–0.4 s, there is the same phase between eL and xm , where eL ¼ s  sL [ 0, xm [ 0, the motor runs in quadrant I. At

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0.6–0.8 s, there is the different phase between eL and xm , where eL ¼ s  sL \0, xm [ 0, the motor runs in quadrant II. At 1.0–1.2 s, there is the same phase between eL and xm , where eL ¼ s  sL \0, xm \0, the motor runs in quadrant III. At 1.4–1.6 s, there is the different phase between eL and xm , where eL ¼ s  sL [ 0, xm \0, the motor runs in quadrant IV.

Fig. 4. Motor angle speed curve

Fig. 5. Electromagnetic and load torque curve

6 Conclusion By combining the backstepping control and the PCH system to solve the problems when the PMSM drive in the four quadrant. In the grid side subsystem, the backstepping controller is designed to the voltage, the current loop has controlled by the PCH controller. The DC bus voltage can be stable without overshoot and the power factor is close to one. The motor angle speed has controlled by the backstepping controller, it can quickly reach the set value and have a good tracking performance. The designed control scheme has a widely prospects by the simulation results.

References 1. Yu H, Yu J (2013) Nonlinear control of induction motors based on state error PCH and energy-shaping principle. Nonlinear Dyn 72(1–2):49–59 2. Arias A, Ortega C, Zaragoza J (2013) Hybrid sensorless permanent magnet synchronous machine four quadrant drive based on direct matrix converter. Int J Electr Power Energy Syst 45(1):78–86 3. Gopinath M, Yuvaraja T, Jeykumar S (2016) Implementation of four quadrant operation of BLDC motor using model predictive controller. Mater Today: Proc 5(1):1666–1672 4. Shao M, Yu H, Yu J (2016) Four quadrant PMSM drive system via single neuron adaptive control and backstepping. ICIC Int 10(2):433–438 5. Wang W, Yin H, Lin G (2009) Study on back-to-back PWM converter based on direct power control for induction motor drive. In: International conference on electrical machines and systems

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6. Magri A, Giri F, Abouloifa A (2010) Robust control of synchronous motor through AC/DC/AC converters. Control Eng Pract 18(5):540–553 7. Cheng X, Yu H (2016) Fuzzy adaptive control of four quadrant permanent magnet synchronous motor servo system. J Qingdao Univ 31(3):18–22 (in Chinese) 8. Yu H, Zhao K (2006) Maximum torque per ampere control of PMSM based on portcontrolled Hamiltonian theory. In: Proceedings of the CSEE, vol 26, no 8, pp 82–87

Sliding Mode Control of Induction Motor Based on AC-DC-AC Converter Huipeng Zhang and Haisheng Yu(&) College of Automation, Qingdao University, Qingdao, China [email protected] Abstract. In order to realize the four-quadrant operation, two-way flow of energy, the grid side unit power factor and guaranteed smooth DC output of the AC-DC-AC converter induction motor drive system, the sliding mode control strategy with variable exponential approach law is adopted. The grid-side converter adopts sliding mode control strategy for variable exponential approach law, which improves the anti-interference and fast response capability of the grid side subsystem. The machine-side converter adopts sliding mode control strategy with variable exponential approach law. When the DC bus voltage of the grid side subsystem tends to output smoothly, then the induction motor side subsystem starts. The simulation results show that the motor runs in four quadrants, two-way energy flow, the control of the DC bus voltage, and the network side operated at unit power are realized. This research has broad prospects in the field of industrial transmission. Keywords: AC-DC-AC converter


Induction motor
Sliding mode control

1 Introduction In recent years, induction motor drive systems of the AC-DC-AC converters have been rapidly developed at home and abroad [1]. High-power four-quadrant converters are used to deliver regenerative energy to the medium-voltage AC grid during train operation, which has good energy-saving effects [2]. Many domestic and foreign scholars have adopted many new control methods in this field through research and analysis. In the paper [3], the vector control method is widely used, but it is difficult to quickly track the grid side DC bus voltage and the speed of the asynchronous motor. In the paper [4], it adopted the method of feedback linearization control, although it can improve the response speed of the system, but it has the disadvantage of complex decoupling matrix [5, 6]. In the paper [7, 8], it adopted the fuzzy adaptive control method. Although it can overcome the impact of motor state switching on the system, it needs a lot of learning record for the state, performance and parameters of the system operation [9, 10], which is difficult to achieve in practice. In order to control two-way flow of energy, guaranteed smooth DC output of the motor drive system of the AC-DC-AC converter. Sliding mode control strategy with variable exponential approach law is adopted. The grid side subsystem adopts the sliding mode control strategy to improve the anti-interference ability of the grid side © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 320–328, 2020. https://doi.org/10.1007/978-981-32-9050-1_37

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subsystem; the machine side subsystem adopts the method to ensure the starting of the machine side subsystem when the grid side subsystem voltage is constant.

2 The Control Principle Framework The control principle framework of induction motor drive systems of the AC-DC-AC converters is shown in Fig. 1.

Fig. 1. The control principle framework

3 The Grid Side Mathematical Model and Control 3.1

Model Representation of the Network Side Subsystem

Model representation of the network side subsystem is expressed as follow 8
< Ldigd
dt ¼ ed  Rigd þ xLigq  ugd Ldi dt ¼ eq  Rigq  xLigd  ugq : gq Cdudc =dt ¼ 2=3ðigq sq þ igd sd Þ  iL

ð1Þ

where, igd and igq are the value of the grid side subsystem current; sd and sq are the d, q axis duty cycle function; ugd and ugq are the transformed values of and f in d and q coordinates, ed and eq are the line voltage of the network side subsystem. x is the grid side subsystem angular frequency, udc is the bus DC voltage. iL is the bus current, measured by the sensor. The three-phase voltage subsystem current and the grid side subsystem is measured by the sensor. 3.2

Design of the Grid Side Subsystem Controller

Voltage control object in the grid side subsystem reaches a given value and remains constant; the unit power factor operation is realized; and the induction motor drive system operates in four quadrants.

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When the converter drive system is running, in order to ensure the robustness of the grid-side subsystem, the voltage error is defined as e1 ¼ Vdc  udc , the sliding-mode surface of the grid-side subsystem voltage loop is selected as Z s 1 ¼ e1 þ k1

t

e1 dt:

ð2Þ

0

where, k1 is the undetermined coefficient, as to further improve the approach speed of the approach law, this paper adopts a new method of variable exponential approach law, we have s_ 1 ¼ e_ 1 þ k1 e1 ¼  g1 jx1 j2 sgnðs1 Þ:

ð3Þ

where g1 is the exponential approach coefficient, g1 jx1 j2 is the variable speed approach coefficient, the sliding mode bandwidth of the shifting approach law is gradually reduced to zero as the jx1 j2 decreases, and finally stabilizes at the
origin.
When the grid side subsystem is stability igq ¼ eq ¼ digq dt ¼ digd dt ¼ 0, in order to obtain the AC-DC-AC converter induction motor four-quadrant drive system running under the unit power factor, the reference value of the current component of the q-axis is selected as igq ¼ 0. We can get the reference value of the d-axis current component is igd ¼ Cudc

k0 e0 þ iL =C þ g0 jx1 j2 sgnðs0 Þ ed  Rg igd

ð4Þ

The grid side subsystem can track the voltage quickly, and the current error of the current loop design of the grid side subsystem is e2 ¼ isd  isd , e3 ¼ isq  isq . The current network side subsystem adopts the integral sliding mode surface with variable exponential approach law and the sliding-mode surface of the grid-side subsystem voltage loop is selected as (

Rt s2 ¼e2 þ k2 0 e2 dt Rt s3 ¼ e3 þ k3 0 e3 dt

ð5Þ

where k2 and k3 are the undetermined coefficient, as to further improve the approach speed of the approach law, this paper adopts a new method of variable exponential approach law, we have (

s_ 2 ¼ e_ 2 þ k2 e2 ¼  g2 jx2 j2 sgnðs2 Þ s_ 3 ¼ e_ 3 þ k3 e3 ¼  g3 jx3 j2 sgnðs3 Þ

ð6Þ

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combining the first formula and the second formula in Eq. (1) with Eq. (6), we can get the controller is (

ugd = ed  Rigd þ xLigd  g2 jx2 j2 sgnðs2 Þ ugq = eq  Rigq þ xLigq  g3 jx3 j2 sgnðs3 Þ

ð7Þ

4 Induction Motor Side Mathematical Model and Control 4.1

Model Representation of Induction Motor Side Subsystem

The mathematical model of the induction motor can be expressed as follow 8 dxm n2p Lm krd sL > > > dt ¼ Jm Lr isq  Jm > > > < dkrd ¼ Lm Rr isd  Rr krd Lr Lr dt ð8Þ disd ¼  ðL2m Rr þ L2r Rs Þ i þ x i þ Lm Rr k þ 1 u > > 2 2 sd s sq rd sd rL L rL L rL > dt s s r s r > > > disq ðL2m Rr þ L2r Rs Þ Lm xr 1 : ¼  i  x i  k þ sq s sd rLs usq rLs Lr rd rLs L2r dt
where, r ¼ 1  L2m ðLs Lr Þ, np is the pole pair number, L is the leakage inductance, xs is the electrical angular velocity, i is current, xm is the mechanical angular velocity, R is the resistance, k is the linkage, the subscripts s and r the stator and rotor of motor respectively, subscripts d and q are the values of the corresponding parameters. Rm is rotor friction coefficient, Jm is moment of inertia, Lm is the mutual inductance of induction motor. 4.2

Design of the Induction Motor Side Subsystem Controller

The purpose of this subsystem is to achieve the induction motor operates normally in four quadrants and flows on both sides of the energy. The induction motor side subsystem uses a sliding mode control strategy that changes the exponential law. Define the tracking error of the speed loop and the flux link as ex ¼ x0  xm , ek ¼ krd  krd and the integral sliding surface is defined as Z t 8 > > e4 dt < s 4 ¼ e4 þ k4 0 Z t > > : s 5 ¼ e5 þ k5 e5 dt

ð9Þ

0

where, k4 and k5 are uncertain coefficients. According to the principle of the sliding mode control method, we have

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(

s_ 4 ¼ e_ 4 þ k4 e4 ¼  g4 jx4 j2 sgnðs4 Þ

ð10Þ

s_ 5 ¼ e_ 5 þ k5 e5 ¼  g5 jx5 j2 sgnðs5 Þ

combining the first formula and the second formula in Eq. (8) with Eq. (10), we can get the reference currents isd and isq of the induction motor are respectively h 8 < isd ¼ Jm Lr g4 jx4 j2 sgnðs4 Þ þ np Lm krd h : i ¼ Jm Lr g jx5 j2 sgnðs5 Þ þ 5 sq np Lm krd

T^L Jm Rr Lr

þ k1 ex

i

krd þ k2 ek

i

ð11Þ

define the error of current loop as esd ¼ isd  isd , esq ¼ isq  isq and the integral sliding surface is defined as Z t 8 > > s ¼ e þ k e6 dt 6 6 < 6 0 Z t > > : s 7 ¼ e7 þ k7 e7 dt

ð12Þ

0

where, k6 and k7 are uncertain coefficients. According to principle of sliding mode control method, we have (

s_ 6 ¼ e_ 6 þ k6 e6 ¼  g6 jx6 j2 sgnðs6 Þ s_ 7 ¼ e_ 7 þ k7 e7 ¼  g7 jx7 j2 sgnðs7 Þ

ð13Þ

combining the third and fourth equations in Eq. (8), we can get the controller of the current loop is h2 i 8 2 2 < usd ¼ rLs Lm Rr þ L2 r Rs isd  xs isq  Lm Rr2 krd þ g6 jx6 j2 sgnðs6 Þ rLs Lr h rLs Lr i : u ¼ rLs ðL2m Rr þ L2 2r Rs Þ isq þ xs isd þ Lm xr2 krd þ g jx7 jsgnðs7 Þ 7 sq rLs L rLs L r

ð14Þ

r

5 Simulation Result The control scheme of the design using Matlab/Simulink is simulated converter drive system operation. Grid side subsystem parameters of converter drive system operation: R ¼ 1 X, Em ¼ 380 V, x ¼ 50 Hz, C ¼ 3300 lF, Vdc ¼ 650 V, L ¼ 15 mH, k1 ¼ 100, k2 ¼ 80, k3 ¼ 55, g1 ¼ 12, g2 ¼ 16, g3 ¼ 11. Induction motor side subsystem parameters of Converter drive system operation: Vn ¼ 380 V, Rs ¼ 0:2147 X, Rr ¼ 0:642 X, Rm ¼ 0:001 N m s, Jm ¼ 0:03 kg m2 ,

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fn ¼ 50 Hz, np ¼ 2, Lr ¼ 0:0852 H, Lm ¼ 0:0813 H, k4 ¼ 20, k5 ¼ 50, k6 ¼ 80, k7 ¼ 60, g4 ¼ 11, g5 ¼ 14, g6 ¼ 10:5, g7 ¼ 11. It can be seen from Fig. 2 that the bus voltage of the grid side subsystem can be guaranteed to be stable at a set value, although the bus voltage will have a certain voltage fluctuation when the induction motor is in the switching operation state, but it can recover quickly and can reach a constant value.

Fig. 2. Grid side subsystem DC bus voltage curve

As can be seen from Fig. 3, the q-axis current component of the grid side is close to 0, the reactive power is 0, and the d-axis current component is positive, thus implementing the cross-crossing transformation, the induction motor drive system operates at unit power factor.

Fig. 3. dq axis component curve of input current of grid side subsystem

As can be seen from Fig. 4, given the rotational speed of a trapezoidal wave, motor speed based on the AC-DC-AC converter can quickly track the set value.

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Fig. 4. Induction motor side subsystem angular velocity curve

As can be seen in Figs. 4 and 5, At 0.4 s–1.2 s, es ¼ se  sL [ 0, xm [ 0; at 1.2 s–1.6 s, es ¼ se  sL \0, xm [ 0; in 2 s–2.8 s, es ¼ se  sL \0, xm \0; in 2.8 s– 3.2 s, es ¼ se  sL \0, xm \0. According to the relationship between the given angular velocity and the actual angular velocity and the relationship between the electromagnetic torque and the load torque, the system realizes two-way energy flow and four-quadrant operation.

Fig. 5. Induction motor side torque curve

As can be seen in Fig. 6, it is obvious that when the system is running, the sliding mode control can ensure that the DC voltage on the grid side reaches a set value and maintains a constant output. Although the voltage fluctuates when the motor state is switched, it quickly returns to the given value. Voltage orientation control has a certain steady-state error, and the error is more and more changed with the state of the induction motor.

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Fig. 6. Voltage comparison curve

6 Conclusion In this paper, aiming at the induction motor drive system, a variable exponential approaching law-sliding mode control method is proposed, which realizes the fourquadrant operation, DC bus voltage controllable, two-way flow of energy, and run at the desired power factor. The subsystem adopts a sliding mode control strategy to improve the dynamic, steady state performance ability of the grid side subsystem, which improves the fast tracking ability of the induction motor speed. The simulation results show that the designed control strategy has superior theoretical significance and application prospects. Acknowledgement. This paper is supported by the Natural Science Foundation of China (61573203).

References 1. Yu H, Yu J, Liu J, Song Q (2013) Nonlinear control of induction motors based on state error PCH and energy-shaping principle. Nonlinear Dyn 72:49–59 2. Shen L, Bozhko S, Asher G et al (2016) Active DC-link capacitor harmonic current reduction in two-level back-to-back converter. IEEE Trans Power Electron 31(10):6947– 6954 3. Majumder R, Ghosh A, Ledwich G, Zare F (2010) Power management and power flow control with back-to-back converters in a utility connected microgrid. IEEE Trans Power Syst 25(2):821–834 4. Zeb K, Uddin W, Haider A et al (2018) Robust speed regulation of indirect vector control induction motor using fuzzy logic controllers based on optimization algorithms. Electr Eng 100(2):787–802 5. Kim DI, Ha IJ, Ko MS (1990) Control of induction motors via feedback linearization with input-output decoupling. Int J Control 51(4):863–883 6. Shao M, Yu H, Yu J (2016) Four quadrant PMSM drive system via single neuron adaptive control and backstepping. ICIC Int 10(2):433–438 7. Vazquez S, Rodriguez J, Rivera M, Franquelo LG, Norambuena M (2016) Model predictive control for power converters and drives: advances and trends. IEEE Trans Ind Electron 99:1

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8. Kumar NS, Sadasivam V, Muruganandam M (2007) A low-cost four-quadrant chopper-fed embedded DC drive using fuzzy controller. Electr Power Compon Syst 35(8):907–920 9. Li S, Zhang W (2017) An adaptive sliding-mode observer with a tangent function-based PLL structure for position sensorless PMSM drives. Int J Electr Power Energy Syst 88:63–74 10. Sebaaly F, Vahedi H, Kanaan HY, Moubayed N, AI-Haddad K (2016) Sliding mode fixed frequency current controller design for grid-connected NPC inverte. IEEE J Emerg Sel Top Power Electron 4(4):1397–1405

Field Environment Intelligent Navigation System for Tomato Transportation Robot Based on Dijkstra Xiaonan Guo1, Yifei Chen1,2(&), Jianwei Zhao3, Liu Yang1, and Wenwen Gong1 1

2

College of Information and Electrical Engineering, China Agricultural University, Beijing, China [email protected] Key Laboratory of Agricultural Information Acquisition Technology (Beijing), Ministry of Agriculture, Beijing, China 3 School of Mechatronic Engineering, China University of Mining and Technology, Beijing, China

Abstract. Tomato fields are large and complex. The picked tomatoes cannot be transported to the storage area in time, which will affect the quality of tomato. Transport tomato not only cost time but also need many labors. So, it is very important to use the tomato transport robot. This paper designed a navigation system for tomato transport robot based on Dijkstra algorithm. The system improved the robustness of the algorithm. The improved algorithm can plan the path for any two points in the field environment. Realized a function that can transport the tomatoes with a best and fastest route. The navigation system defining the route and the intersection. The routes and intersections of the filed can be added and modified flexibly that adapt to changeable field environment. Achieving the human-computer interaction interface with Java, which make the operation easier and more flexible. It is very important that the real-time performance of the robot. The average calculate time of this navigation system is about 0.53 s, which conform to real-time requirements of transportation robot. Keywords: Tomato transport robot


Navigation
GPS sensor
Dijkstra

1 Introduction The key technologies of transportation robot are guidance, location and path planning. How to get the coordinate information of the starting point and how to reach the target point in the fastest and optimal way. Shortest path planning for transportation robot: In literature [1] OpenCV was used for polygon fitting of obstacle contour. Using the network topology to establish the working model, try and Error method was combined with Dijkstra algorithm to obtain the approximate optimal path. Literature [2] uses the improved discrete particle swarm optimization algorithm to plan the optimal path of the robot. Literature [3] proposed different optimization algorithms to solve the shortest path planning for difficult scene. © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 329–336, 2020. https://doi.org/10.1007/978-981-32-9050-1_38

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This paper taking Dijkstra algorithm as the core designed a tomato transportation robot navigation system for tomato field in Fig. 1, which make the tomato transportation more cost effective and more intelligent.

Fig. 1. Tomato field

The navigation system of tomato transport robot designed based on Dijkstra algorithm. GPS sensor is used to locate the robot. The system uses Java to realize the shortest path algorithm and displays the path in the human-computer interaction interface. And the path information will be integrated into instructions and transmitted to the robot controller. The system adapts to the changeful field environment by independently defining the connected route and intersection node. The functions of modifying route and node are designed to adapt to the changing environment. The system is very flexible and more suitable for the tomato transport robot in the field environment. The flowchart as shown in Fig. 2.

Fig. 2. The flowchart of tomato transport robot navigation system

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2 Dijkstra Shortest Path Planning Algorithm Dijkstra algorithm using the directed graph to solve the shortest path planning. The key feature of Dijkstra is expanding from the starting point until reaches the ending point. The key of calculating the shortest path is: A adjacency matrix V is defined to store the distance information between any two points in the weight graph. If the two points are connected, then the matrix value is the distance between these two points, otherwise it
is ∞. The distance between a point and itself is 0. Creating a set of information j dj ; pj ; fj for each point v. The dj is the distance between point s and point j; pj is previous node of j; fj records whether the dj value is ∞. The Fig. 3 is Dijkstra algorithm flowchart.

Fig. 3. Dijkstra algorithm flowchart

Dijkstra’s basic procedure is: 1. Initialization: The starting point s is set as: ds ¼ 0, ps is empty, fs ¼ true. The other nodes iðnÞ: diðnÞ ¼ 1, piðnÞ is empty, fiðnÞ ¼ false, and search from s. 2. Calculate diðnÞ value of all points which fiðnÞ ¼ false: di ¼ minfdi ; dk þ lg; l is direct distance that from node j to node i, k is previous node of j. 3. Select the next node: for all fiðnÞ ¼ false points which found in step 2, sorting their diðnÞ and selecting the closest point i as the current point and set pi ¼ j, fi ¼ true. 4. Determine whether the fe of terminal e is true. If yes, output the shortest path. If no, return step 2. The navigation system constructs the model of the field as the adjacency matrix in the algorithm. Then, using the Dijkstra algorithm to plan the way between two

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intersections in the field. The adjacency matrix contains all of intersections, however, the robot and the tomato may appear at anywhere. So, this paper improves Dijkstra algorithm. The improved Dijkstra algorithm can calculate the shortest path of any points in the map.

3 Design of Navigation System for Tomato Transportation Robot 3.1

Hardware Design of Tomato Transportation Robot

The tomato transport robot in this paper adopts a four-wheel structure (as shown in Fig. 4). The tomato transport robot is equipped with camera, ultrasonic sensor, infrared sensor, GPS sensor and so on.

Fig. 4. Model of tomato transportation robot

The tomato transportation robot control system adopts the interactive structure of host computer and robot. The host computer providing functions include receive data, plan path, data management, robot motion monitoring and others. The robot to be responsible for robot control, data acquisition and environment perception. The communication between host computer and robot is through WIFI. Figure 5 is hardware structure of tomato transport robot.

Fig. 5. The hardware structure of tomato transport robot

Field Environment Intelligent Navigation System

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333

Navigation System Path Planning

The navigation system improves Dijkstra algorithm. The operator gets the location of the robot as starting point and set the position of tomato or storage area as ending point. The system plans the path between two points and calculates the route length and path information, which are displayed in computer. Improved algorithm flowchart as shown in Fig. 6.

Fig. 6. The flowchart of navigation algorithm

Improved algorithm first checking whether the starting point and ending point are stored in the adjacency matrix, and then determines the node which will be used for calculation. Use the JDBC API executing SQL statements to find if the start and end points exist in the database and return the data id.

id="select id from vertex where address="+"\""+add+"\"";

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If id = 0, then the point does not exist in the data, the distance between this point and other points in the database are calculated use Eq. (1). d¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðx1  x2Þ2 þ ðy1  y2Þ2

ð1Þ

The closest point’s id will be returned. Using this node as the current starting or ending point to calculate the shortest path. The system stores the intersections as nodes. For example, the positions of two ends of each ridge and storage area. As shown in Fig. 7. Then store all routes. The distance between two adjacent points is actual distance, and the distance between non-adjacent is ∞. Including the length of a ridge, the distance between the end of the ridge and storage area. The algorithm converts all nodes and route information into arrays and adjacency matrices. Selecting the position of the robot or the position of the tomato as starting point and select the position of tomato or storage area as ending point.

Fig. 7. Schematic of tomato field

3.3

Navigation System Functions

The navigation system designed in this paper realized the path planning, path and node management, path monitoring. The operator selects the position of tomatoes as ending point through the human-computer interaction interface and select the robot current position as starting point to plans the optimal route. The navigation system functions include node management, path management, optimal path calculation, robot motion monitoring. Node Management Section. Node management section has four functions: node add, node delete, node modify, view node information. In the computer interface click any intersection to add new node to the database. Unusable branch roads also can be removed from node management. Path Management Section. Path management section has four functions: path add, path delete, path modify, view path information. Add the new road information to the

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database in the human-computer interaction interface. Operators also can delete unavailable paths. Optimal Path Calculation. Calculation shortest path section consists of three parts: (1) calculate the shortest path between two intersections in the map. (2) calculate the way between the robot’s position and tomato position. (3) calculate the shortest path from the robot’s position to storage area. Robot Motion Monitor. Robot motion monitor part includes two parts: robot position monitor and robot motion route monitor. The robot position monitor part can display robot current position in real time so that the operator dispatch robot. The robot route monitor part display of the robot’s motion on the human-computer interaction interface.

4 Experiment The field tomato transportation robot navigation system was tested in the laboratory. First. Existing nodes are selected as starting and ending point for path planning. The shortest path will be planned according to current node and return path information. There are many routes between two points. The algorithm calculates the shortest distance as the current route. This path planning takes 0.5 s. Second. Starting from the current position of robot and select ending point on the map for path planning. The starting point and the ending point are not in the database. So, the closest point to the starting point and ending point will be found, which will be used to calculate the shortest distance and return the path information. This path planning takes 0.6 s. Third. Take the current position of robot as starting point and select exist node as target point for path planning. The starting point is not in the database. So, calculate the closest point to the starting point and use it to calculate the shortest distance. This path planning takes 0.48 s. Fourth. Select exist node as the robot current location and select ending point on the map. The ending point is not in the database. So, calculate the closest point to the ending point and use it to calculate the shortest distance. This path planning takes 0.53 s. Through the simulation test, the function of this navigation system is stable. The average calculating time of improved shortest path algorithm is 0.53 s, which conform to the real-time requirements of the robot. The result of shortest path planning is accurate. And the system has good adaptability to complex environment.

5 Conclusion The navigation system designed in this paper is based on the improved Dijkstra algorithm. Using the GPS to obtain the robot position. Realizing the information interaction between host computer and robot through the WIFI module. Realizing the

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shortest route planning between any two points of tomato field. The man-machine interactive interface was designed to monitor the robot’s motion and to manage tomato field map information. The test environment of simulation experiment is complex, there are many intersections. The system still realizes the function of shortest path planning and navigation accurately. The tomato field environment is simple. It is expected that the tomato transport robot navigation system can achieve better path planning effect. In the following research, the system will be tested in the tomato filed. And the navigation algorithm will be improved to make the path calculation more accurate and reduce the time and space complexity of the algorithm.

References 1. Li D (2014) Dijkstra’s algorithm in AGV. In: 2014 IEEE 9th conference on industrial electronics and applications, pp 1867–1871. https://doi.org/10.1109/iciea.2014.6931472 2. Sun C (2010) Research on optimal path planning for robots based on discrete particle swarm optimization algorithm. East China Jiaotong University. (in Chinese) 3. Klidbary SH, Shouraki SB, Kourabbaslou SS (2017) Path planning of modular robots on various terrains using Q-learning versus optimization algorithms. J Intell Serv Robot 10:121– 136. https://doi.org/10.1007/s11370-017-0217-x 4. Parulekar M, Padte V, Shah T, Shroff K, Shetty R (2013) Automatic vehicle navigation using Dijkstra’s Algorithm. In: 2013 international conference on advances in technology and engineering, India. https://doi.org/10.1109/icadte.2013.6524721 5. Yang X, Liu D, Cong L, Liang L (2014) Shortest path algorithm based on distance comparison, Canada. IEEE. https://doi.org/10.1109/igarss.2014.6947142 6. Jiang J, Wu L (2014) A re-optimization dynamic shortest path algorithm for vehicle navigation, Canada. IEEE. https://doi.org/10.1109/igarss.2014.6947135 7. Zhang Y, Chong KT (2014) An GPS/DR navigation system using neural network for mobile robot. J Int J Precis Eng Manuf 15:2513–2519. https://doi.org/10.1007/s12541-014-0622-4

Reducing Bullwhip Effects in Supply Chain Systems via H 1 Control Chen Qian and Qingwei Chen(&) School of Automation, Nanjing University of Science and Technology, Xiaolingweistr. 200, Nanjing 210094, China [email protected] Abstract. To solve the bullwhip effect on supply chain members, H1 control is used to inhibit the bullwhip effect and ensure the stable operation of the system. Focusing on a member in the supply chain, a linear discrete-time system composed of a controlled object and a steady controller was constructed. Because the parameters of the bullwhip effect have the same value with the H1 norm of the system, the robust controller was introduced into the system and replaced the prediction function. This method can effectively solve the problem of order amplification caused by the variation of customer demand. The simulation results show that compared with the traditional prediction algorithm, the H1 controller proposed in this paper can more effectively attenuate the bullwhip effect caused by fluctuations of customer demand. Keywords: Bullwhip effect

 H1 control  Supply chain

1 Introduction Effective supply chain management is driven by the market demand [1]. Coordinating between supply and demand is always the top priority of SCM. The bullwhip effect is formed by supply chain members trying to coordinate supply and demand [2]. The bullwhip effect refers to the phenomenon that demand fluctuations are amplified during the supply chain transmission process. The research on the bullwhip effect has been mainly divided into: the causes of the bullwhip effect, the measurement of the magnification effect and how to attenuate. Lee et al. [3, 4] analyzed the causes of the bullwhip effect. There were two main methods for quantifying the bullwhip effect. One was the statistical analysis measurement method published by Sterman. The other one was a new bullwhip effect quantification method based on control engineering proposed by Disney and Towill et al [5]. They used the transfer function, frequency analysis and spectrum analysis methods to treat the supply chain system, and defined the ratio of the Laplace transforms (demand and order) as the quantitative index of the bullwhip effect. Xiaoyuan [6] researched the classical supply chain system and introduced the robust H1 control. Furthermore, Xiaoyuan [7] established a discrete-time system model with multiple delays, quantified the bullwhip effect using variance, and proposed the H1 control method when the worst-case scenario occurred. © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 337–346, 2020. https://doi.org/10.1007/978-981-32-9050-1_39

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This paper firstly used the engineering control theory method to introduce the H1 controller instead of the prediction function to predict the demand for the first-order supply chain system. Then the system model with H1 controller was constructed, and the Riccati-based optimization algorithm was used to solve the controller to achieve the purpose of attenuating the bullwhip effect. Finally, through MATLAB, the simulation showed that the H1 controller method was better than the time average method and the exponential average method for the actual demand data. No research has solved the bullwhip effect by combining H1 control theory focusing on one node in supply chain. Therefore, this study has important significance and can provide a useful reference for the weakening of the bullwhip effect in marketing.

2 Model and Measurement 2.1

Structure of Supply Chain Systems

H1 control theory is a method of analyzing the bullwhip effect, as it can be transformed into a control model. Using control theory, the supply chain system is divided into two parts, the generalized plant and the stabilizing controller. The control model is shown in Fig. 1.

Fig. 1. General closed-loop interconnection model

The core company predicts future order based to the demand information of downstream requests, and then books orders to upstream company. For the entire control model, demand DðtÞ is the external input represented by w, while demand DðtÞ is also the measurement variables of the stabilizing controller represented by y. Furthermore, order OðtÞ is the external output of the control mode represented by z, ^ ðtÞ is a control signal represented by u. Disconnecting the and forecasting demand O controller, the open-loop interconnection of supply chain system is described as:





 OðtÞ DðtÞ P11 ðzÞ P11 ðzÞ DðtÞ ¼P ¼ ^ ðt Þ ; DðtÞ Sð t Þ P11 ðzÞ P11 ðzÞ O

ð1Þ

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the controller follows the equation: ^ ðtÞ ¼ K ðzÞDðtÞ: O

ð2Þ

In the supply chain, we focus on the decision of order OðtÞ. 2.2

Order Decision Rule

To quantify the magnification of order volatility, suppose the chain only has a single retailer and a manufacturer. It can be assumed that the three steps occur in each time period t: first step, the retailer obtains the commodity ordered before several time periods. Second step, the retailer observes and satisfies the demand DðtÞ from the downstream (assuming there is no backlog order). Third step, the retailer updates the inventory and orders an order OðtÞ from the upstream. The time delay factor is considered in this system. It can be understood that the retail applies for an order at the third step in period t and receives goods at the first step in period t + L. Here L is considered to be a fixed constant. According to the time series, the retailer receives the goods in the next period even if there are no production and logistics delays. The lead time L is equal to TP þ 1, where TP refers to the production or logistics delay. In this paper, it is assumed that TP equals to 3. This time delay is common in models, not a special case of this model, and has no direct impact on the results. For retailers, we used a generalized replenishment rule strategy to meet market rules, and the order decisions are following the equation: ^ ðtÞ þ K1 ðTNSðtÞ  NSðtÞÞ þ K2 ðDWIPðtÞ  WIPðtÞÞ: OðtÞ ¼ O

ð3Þ

^ ðtÞ is During the period t, OðtÞ is the order quantity determined at the third step, O the forecasting demand. NSðtÞ equals the net stock on hand, while WIPðtÞ refers to the work in process position. TNSðtÞ is the target net stock, and the desired WIP level is represented by DWIPðtÞ. K1 and K2 are key parameters of the decision rules. ^ ðtÞ is updated every period by the stabilizing controller. The forecasting demand O According to the events, NSðtÞ and WIPðtÞ are updated by following equations: NSðtÞ ¼ NSðt  1Þ þ Oðt  LÞ  DðtÞ WIPðtÞ ¼ WIPðt  1Þ  Oðt  LÞ þ Oðt  1Þ:

ð4Þ

^ ðtÞ in TNSðtÞ is the safety stock for the company, which is approximately equal to O actual production. It is changed according to the forecasting demand of each period. ^ ðtÞ. DWIPðtÞ is also related to the forecasting demand, and DWIPðtÞ ¼ ðL  1ÞO

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Fig. 2. Causal loop diagram

2.3

Transfer Function Model

Using the control theory, the supply chains are viewed as systems, and the relationship between different nodes in the chain are modelled by the transfer function. For one order decision with any parameters, a transfer function is determined. The input is the demand from customer and the output is the corresponding order decision. To describe the order decision rule, the supply chain event ‘causal loop diagram’ was drawn in Fig. 2.

Fig. 3. Block diagram

Next, based on the causal loop diagram, the ‘block diagram’ was shown in Fig. 3. A delay factor z1 is used to ensure the time series matches the actual situation. Finally, applying the block diagram to simplify the well-established rules, we obtained the following equation to calculate the order OðtÞ, over the observed demand DðtÞ and ^ ðtÞ: forecasting demand O




2.4

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 " z4 OðtÞ ¼ z4 þ ðK2 1Þz3 þ K1 K2 DðtÞ 1

ð5Þ

ðz4 z3 Þð1 þ 3K1 þ K2 Þ z4 þ ðK2 1Þz3 þ K1 K2

#


0

 DðtÞ ^ ðtÞ : O

Bullwhip Effect Metrics

The performance of a system is often expressed in terms of frequency response (FR). Use the sine waves of different frequencies as the input. Since the supply chain systems are linear discrete-time systems, the amplitude of output will be different. This paper focused on the Amplitude Ratio (AR), the ratio of the amplitudes. For different frequencies, AR was calculated, and FR plot was drawn to determine the dynamic characteristics of the system. The FR was used to predict the multiple of the variance amplification below the different frequencies. The bullwhip effect metric peak AR value is defined as: ARP , maxx2ð0;pÞ ðGðjxÞÞ;

ð6Þ

where GðzÞ is the transfer function of the whole system. For a particular frequency, the amplitude of the output sinusoid is the largest. At the same time, the peak AR value takes the maximum value which is the worst case. In control system engineering, the H1 norm of the system is defined as: kGðzÞk1 , sup rmax ðGðjxÞÞ; x

ð7Þ

which is the maximum singular value of the frequency response, so it is the same as the ARP. The H1 norm is also the metric of the system, which provides an ideal for the introduction of the H1 controllers into the supply chain.

3 Attenuating the Bullwhip Effect via H 1 Control As previously mentioned, the control model of the supply chain systems was analyzed and the block diagram shown in Fig. 3 was obtained. The controller was designed to attenuate the bullwhip effect. According to the transfer function, this system is a generalized linear discrete-time system, and is described as: ^ ðtÞ; xðt þ 1Þ ¼ AxðtÞ þ B1 DðtÞ þ B1 O ^ ðtÞ; OðtÞ ¼ C1 xðtÞ þ D11 DðtÞ þ D12 O ^ ðtÞ: DðtÞ ¼ C2 xðtÞ þ D21 DðtÞ þ D22 O

ð8Þ

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To solve this H1 discrete-time control problem, for a pre-specified positive value c, a stabilizing controller K ðzÞ should be calculated to meet next equation: kFl ðP; K Þk\c;

ð9Þ

where Fl ðP; K Þ is the transfer function FOD ðzÞ from the demand DðtÞ to the order OðtÞ, as: FOD ðzÞ ¼ Fl ðP; K Þ ¼ P11 þ P12 K ðI  P11 K Þ1 P21 :

ð10Þ

Petkov [8] solved this problem that computed a H1 suboptimal controller. According to the Ricatti-based H1 method, the parameters of the controller were calculated, further the transfer function of the system was obtained.

4 Simulation Consider the case of the parameters: K1 ¼ 1:2; K2 ¼ 0:7. By Eq. (5),


 " 7z4 OðtÞ 10z4 þ 2z3 5 ¼ DðtÞ 1

53z4 53z3 10z4 þ 2z3 5

#


0

 DðtÞ ^ ðt Þ : O

Next, the parameters of H1 controller were obtained. 2

0:0000 6 1:0206 Af ¼ 6 4 0:0000 0:0000 Cf ¼ ½ 0:0689

0:0000 0:0000 0:0000 0:0000

1:0142 0:0345 0:0000 0:000

3 2 3 0:1101 0:1891 6 7 0:1348 7 7; Bf ¼ 6 0:2316 7; 5 4 0:2024 0:0193 5 0:6965 0:7436

0:0844

0:0582

0:0208 ; Df ¼ 0:1034:

It can be confirmed that the controller was stable and the transfer function of the whole system was obtained. Then, the FR plot was drawn as shown in Fig. 4. The red dotted line indicated the frequency response of the system without the H1 controller, while the blue line indicated the frequency response including the H1 controller. We can find that the amplitude ratio (AR) of the closed-loop was always smaller than the amplitude ratio of the system without the H1 controller. For the index of the bullwhip effect, ARP ¼ 1, the controller met the requirements for attenuating the bullwhip effect, and the closed-loop system was asymptotically stable.

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Fig. 4. Frequency response of supply chain system

The H1 controller method, exponential smoothing method and moving average method were used to simulate the actual demand data of the same group (sales records of commodities on Taobao). As shown in Fig. 5, (a) was the result of the H1 controller using the parameters calculated above; (b) and (c) were the results of predicting the demand using the exponential smoothing method [6], which the parameters were Ta ¼ 8ð8=1=1Þ and Ta ¼ 8; Tn ¼ 4; Tw ¼ 4; ð8=4=4Þ) respectively; (d) was the result of the moving average method [6], which set the parameter, Tm ¼ 10. The solid blue line indicated the actual demand and the red dotted line indicated the order to the upstream. The fluctuations in the number of orders in Fig. 5(a) and (c) were less than the fluctuation in actual demand, while the fluctuation of order quantity in Fig. 5(b) and (d) were lager than the fluctuation of demand. For the variation of the demand signal, the H1 control method and the exponential smoothing method with appropriate parameters can effectively reduce the bullwhip effect. To more intuitively compare the four sets of data, Table 1 gave the SDs of demand and four sets of orders and the ratios of orders to demand. From Table 1, using exponential smoothing method (8/1/1) and the moving average method, the fluctuations of the order quantity were magnified. H1 controller method performed best, and the volatility of order was almost one-third of the volatilities of exponential smoothing (8/1/1) method or moving average method. Figure 6(a) and (b) were the responses of H1 controller method and exponential smoothing method (8/4/4) to the step demand respectively. How long to keep order quantity to meet the new demand determines the quality of the forecasting algorithm. By analyzing the data in Fig. 6(a) and (b), it can be seen that the H1 controller algorithm required 27 time intervals so that the difference between order and demand was less than 0.2% of demand, while the exponential smoothing method (8/4/4) required 54 time intervals. The H1 controller method had a better performance in this respect.

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Fig. 5. Impact of H1 controller or prediction functions on real demand

Table 1. SDs and bullwhip effect metrics of H1 controller and prediction functions Standard deviation Metric c Demand 457.0 1 Order (H1 controller) 246.5 53.95% Order (exponential smoothing (8/1/1)) 706.3 154.55% Order (exponential smoothing (8/4/4)) 295.8 64.74% Order (moving average) 712.5 155.91%

Figure 6(c) and (d) showed the responses of the H1 controller method and exponential smoothing method (8/4/4) to sinusoidal demand at different frequencies respectively. From Fig. 6(c) and (d), the bullwhip effects reduced at the high frequency part indicated by green dotted line in both two methods. Whereas the H1 controller method performed better than the exponential smoothing method (8/4/4) at the low frequency part indicated by green solid line. The order quantity in H1 controller method can follow the change of demand, while the exponential smoothing method (8/4/4) in this area fluctuated significantly in order quantity, and the bullwhip effect has not been reduced. In general, the H1 controller method had a better effect than the exponential smoothing method (8/4/4), especially in the case of small fluctuations in demand.

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Fig. 6. Impact of H1 controller and exponential smoothing method for different patterns

So far, the model built were all one layer, but the actual supply chain was multilayered. If all nodes in the supply chain use the same parameters, the data of the order volume fluctuations on the second and third layer with the demand data from Fig. 5 were organized in Table 2. Compared with the initial demand data, the bullwhip effect metrics on the second layer and the third layer were 44.61% and 43.63%, respectively. The bullwhip effect has been reduced layer by layer. Table 2. SDs and bullwhip effect metrics on different layers Demand Order (first layer) Order (second layer) Order (third layer)

Standard deviation Metric c 457.0 1 246.5 53.95% 203.8 44.61% 199.4 43.63%

5 Conclusion Focusing on the demand signal processing, the supply chain was regarded as a control system as well as the H1 controller was designed to weaken the bullwhip effect. In addition, according to the order decision rule, the causal loop diagram of the events was

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obtained and then the block diagramed was determined in combination with the H1 controller. The controller parameters were calculated by solving the H1 optimal problem of the discrete-time system. Finally, the H1 controller method is simulated and compared with the time average method and the exponential smoothing method. The simulations reveal that the H1 controller method perform best in attenuating the bullwhip effect. At the same time, the order can quickly track the changes in demand, greatly reducing the inventory risk and cost of the enterprise. Acknowledgment. This work was supported in part by National Natural Science Foundation of China under grant 61673217, 61673219, in part by Postgraduate Research & Practice Innovation Program of Jiangsu Province under grant KYCX19_0300, KYLX16_0450, KYCX19_0301, KYCX19_0299.

References 1. Christopher M (1992) Logistic and supply chain management: strategies for reducing costs and improving services. Pitman Publishing, London Financial Times 2. Burbidge JL (1983) Five golden rules to avoid bankruptcy. Prod Eng (11):824–987 3. Lee HL, Padmanabhan V, Whang S (1997) Information distortion in a supply chain: the bullwhip effect. Manag Sci 43(4):546–558 4. Lee HL, Padmanabhan V, Whang S (1997) The bullwhip effect in supply chain. Sloan Manag Rev 38:93–102 5. Sterman JD (1989) Modeling managerial behavior: misperceptions of feedback in a dynamic decision-making experiment. Manag Sci 35(3):321–339 6. Xiaoyuan H, Haifeng G, Zhen L (2007) A H∞ control method of the bullwhip effect for a class of supply chain systems. Int J Prod Res 45(1):207–226 7. Xiaoyuan H, Haifeng G, Zhen L (2005) A H∞ control method of the bullwhip effect for a supply chain delay model. J Syst Eng 20(6):585–590 8. Petkov PHr, GU DW, Konstantinov MM (1999) Fortran 77 routines for H∞ and H2 design of discrete-time linear control systems. In: NICONET.WGS 9. Bengang G, Youming C (2002) The cause of “bullwhip effect” and its solution. Shanghai Manag Sci 3:38–40 (in Chinese) 10. Li Y (2002) Robust control: linear matrix inequality method, 1st edn. Tsinghua University Press, Beijing (in Chinese)

Visual-Inertial Localization and Map Summarization Based on Prior Map Bo Fu1,2, Yanmei Jiao1,2, Xiaqing Ding1,2, Yue Wang1,2(&), and Rong Xiong1,2(&) 1

2

Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China [email protected], {wangyue,rxiong}@iipc.zju.edu.cn State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China

Abstract. Accurate localization of the robot is an important prerequisite for the autonomous mobile robot. Existing localization methods struggle with the cumulative drift errors problem. We proposes the visual-inertial localization method which based on multi prior maps and generates a summary map with a fixed map size. Specifically, for the localization problem with gravity alignment, the relative pose of the pitch and roll is known, which reduces the dimensions of the problem. For the 3D-2D data association of the map and current query image, both geometric and reprojection constrains are used. In the process of map summarization, the idea of iterative map building is proposed and a novel scoring strategy is exploited to limit the summary map to a fixed size. We evaluated our method on public and our datasets. The result indicates that our method owns higher localization accuracy and better robustness than other comparative method. Keywords: Visual-inertial localization


Map summarization
Priori map

1 Introduction Large-scale, long-term distributed mapping and localization is a core challenge in the robotics and mobile device applications [1], which can be widely used in scenes such as inspection robots and augmented reality. In recent years, Simultaneous Localization And Mapping (SLAM) [2] has developed rapidly. SLAM refers to create an environmental map while moving, and estimate the pose of the robot itself. EKF-SLAM was first proposed by [3], the motion model and the observation model are used to calculate the pose of the robot itself and the landmark points. However, EKF-SLAM relies on the correct data association. Once the data association is wrong, it will lead to the failure of building the map. Moreover, the calculation cost of EKFSLAM is high, and the calculation amount is proportional to the square of the map size, so it can only be used for a smaller range of environments. [4] proposed the ORBSLAM algorithm that has three parallel threads of tracking, mapping and loop closure. It enables high-precision localization in real time online in small or wide range of unknown environments (Fig. 1). © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 347–355, 2020. https://doi.org/10.1007/978-981-32-9050-1_40

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Fig. 1. Localization effect diagram: blue frame is the robot pose; lines of different colors are the lines of sight in the current pose, and the different colors represent the 3D points from different maps; the black points are the priori map points.

However, SLAM system cannot perform robust localization in complex environments, and in many cases the localization accuracy cannot meet the application requirements. Therefore, the localization method based on a priori map is proposed as shown in Fig. 2. [5] proposed a distributed and decentralized robot mapping method which allows to access to the map during operation. In order to increase localization accuracy, algorithms must be developed to achieve better results in repeated access to the same place. [6] proposed and evaluated an algorithm for alternating between online location identification and map maintenance offline. Similar to [7, 8] directly calculates the pose matrix between the map and the current trajectory. It uses geometric constraints between 3D point clouds, eliminating the need for matching between feature points, reducing the possibility of mismatching and stability. However, the matching between point clouds is generally suitable for laser data, and requires a dense point cloud with small uncertainty, which will cause a decrease in localization accuracy when used in visual data. We proposes the visual-inertial localization method which based on multi prior maps and generates a summary map in the fixed size. For the data association of the map and the current query image as shown in Fig. 2, both geometric and reprojection constrains are used, and a final RANSAC is used to filter the inliers. In the process of map summarization, the idea of iterative map building is proposed, and the global highprecision visual-inertial map is obtained through continuous iterative optimization. To limit the summary map to a fixed size respect to the area covered, a novel scoring strategy is exploited to compress the map, so that the global map size can converge after multiple iterations.

2 Data Association and Optimization In this section, we will discuss how to match current query image feature points to the priori map 3D points quickly, efficiently, and accurately. After completing the data association between the query image and the priori map, we will discuss how to

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Fig. 2. System overview: A schematic is drawn on two places, the yellow line is the line of sight of the robot, and the current continuous image is associated with the data of the 3D point of the priori map to complete localization.

optimize the localization result using the observation information of the prior map. The priori map we saved includes the pose of the keyframes at each moment, and the position of 3D points observed by the keyframes at each moment. As shown in Fig. 3, our data association consists of four steps: 2D-3D, geometric constraint, 3D-2D, reprojection constraint. 2.1

Data Association

2D-3D and Geometric Constraint: In most datasets, the pre-built map is very large. It is time-consuming and unreasonable to traverse all the 3D points for data association. Since the 3D points in the priori map are all related to the keyframes, and the number of keyframes is much less than the number of 3D points, we first associate the current keyframes with the priori map keyframes. In our method, the relative pose (i.e. G TC ) of the current trajectory in initial moment to the priori map is known by default, which can be rough estimated by mechanical parameters. After the conversion by G TC , the 10 keyframes in the priori map that are closest to the current keyframe in Euclidean distance are searched. The above search can be accelerated using the KD-tree [9]. The priori 3D points observed by these 10 priori keyframes are defined as candidate points. After the keyframe-to-keyframe filtering, we complete the point-to-point data association. Specifically, the pixel points on the current query image are reconstructed into 3D points. Each 3D point is then filtered from the candidate priori 3D points with Euclidean distance as the threshold. After this, a rough data association is completed, since the keyframe-to-keyframe filtering is performed first, then perform the point-topoint filtering is performed, which greatly reduces the time for data association, and the accuracy is not reduced. 3D-2D and Reprojection Constraint: After 2D-3D projection and geometric constraints, the data association can be further filtered. We reproject the priori map 3D point onto the current query image, and calculate the pixel error of the closest feature point detected in current image, and filter with the pixel error as the threshold.

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Fig. 3. Data Association: The pixels on the query image are reconstructed in 3D (2D-3D), and then filtered with the priori map 3D points in distance (Geometric Constraint). Then, the priori map 3D points are projected onto the current query image (3D-2D), filtered with the current query image points in reprojection error (Reprojection Constraint). The priori map 3D points remaining after filtering are associated with the query image points.

After the above filtering step, the pixel points on the current image are associated with the priori map 3D points. In order to reduce the influence of mismatch as much as possible, we remove the outliners in the priori map 3D points by the RANSAC algorithm. 2.2

Optimization

Next, we put the priori map 3D point observations in the optimization framework to optimize the pose of the current robot. As shown in Fig. 4, both the priori map 3D point and the currently reconstructed 3D point are taken together as observations to participate in the local optimization of the current pose. And according to the credibility of the priori map information, we can weight the priori map 3D points in the optimization, and even let their values not be updated. Thanks to the observability of pitch and roll angle given by the inertial measurements, the direction of gravity can be aligned between prior map coordinate system and current localization coordinate system, leading to the reduction of DoF in pose as W

 x ðcÞjð t1 TC ¼ ½Rz ðaÞRy ðbÞR

t2

t3 ÞT 

where a and (t1 t2 t3) denote the yaw angle and three translations to be estimated. So our optimization function only needs to update the pose of 4DoF, which greatly improves the optimization speed.

3 Map Summarization In this section, we will discuss how to build a map to maintain its accuracy while the map size is maintaining constant of the scenes.

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Fig. 4. The schematic diagram of the optimization framework, the abrupt frame is the priori map information.

3.1

Recursive Mapping

In order to make the built map as accurate as possible, we propose a recursive mapping method, for example, MH02 data localize on the MH01 map to build the MH0102 map, then the MH03 data localize on the MH0102 map to build the MH010203 map, and so on. On the premise that there are multiple trajectories, the difference between direct fusion mapping and recursive mapping is that direct fusion mapping optimizes multiple trajectories together, which will cause the trajectories to be pulled each other, thus affecting the accuracy of the map, and it does not suitable for most real-world scenarios. The advantage of recursive mapping is that in the process of updating the map in each iteration, a certain degree of confidence is given to the priori map, thereby narrowing the scope of optimization and avoiding over-optimization. In the process of localization each time, the map is updated in real time, which is in line with the actual situation, and the algorithm can be applied to the real scene. As shown in Fig. 5, the robot is localized on a pre-built map, and when it moves to a location that has not been previously visited, it starts to localize itself and build a new map.

Fig. 5. Recursive mapping schematic diagram: the blue frame is the robot pose, the lines of different colors are the line of sight of the robot in the current pose, the different colors represent the 3D points from different maps, the black points are the a priori map points, and the red points are the currently built points.

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Map Sparsification

In order to maintain the size of the map, the map information needs to be compressed when performing recursive mapping of multiple trajectories. The information in the pre-built map is mainly: 3D map landmark points, keyframes. The conventional compression method is to delete duplicate or low information data. First, score all the map points. If the map points are observed by multiple keyframes, the score will be high, otherwise it will be low. In order to avoid those map points that were only seen once but provide a lot of information, we set the lower bound of the score. Points below the lower bound of the score are also retained. In this way, only map points that are observed less frequently and provide less information are deleted. Deleting only map points with low observations is not enough to make the map converge with iterations. Therefore, it is necessary to select each newly added map information. It can be known in the data association section that the priori map 3D points associated with the current image are duplicated with the 3D points of the current trajectory. Therefore, we mark the associated 3D map points and delete these 3D points after the localization is completed, which will not affect the localization effect and maintain the size of the map.

Fig. 6. Schematic diagram of map sparsification.

For a track segment consisting of multiple keyframes, it can be classified into novel track segments, and redundant track segments. In practical applications, 3D map landmark points and track segments are tightly coupled. When generating multiple trajectories to build a map, we set a sliding window with a window size of 2. When the track segment enters the sliding window, it is judged that if a certain number of new map points are observed at the track segment, the track segment is considered novel. If not, the track segment is considered redundant. For the novel track segment, we will keep it. For the redundant trajectory segment, we will delete it and its observed map points. As shown in Fig. 6, we can see that the denser part of the map deletes more points, which are points that provide less information. The more sparse parts of the map delete fewer points because the number of points in this part is small but providing enough information. After the map sparsification, the built map has a clear structure, and it’s size constant with the scences.

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4 Experimental Results To verify the accuracy of our method, we performed experiments on the EuRoC MAV dataset [10] and the YQ dataset, respectively. The EuRoC MAV dataset comes from ETH. It uses the quadrotor equipped with VI-Sensor to collect data and provides ground-truth as shown in Fig. 7. Its target scene is robot rescue, so the scene is more complicated and requires high reliability for visual algorithms. In the test process of the localization algorithm based on the visual-inertial prior map, the trajectory of MH01 and MH03 is used to build the map MH0103, we completed the localization of MH02 on the a priori map of MH0103.

Fig. 7. Sensor configuration.

Fig. 8. Schematic diagram of trajectory accuracy.

The YQ data set is an outdoor data set collected by the mobile robot developed by our laboratory on the Yuquan Campus of Zhejiang University. The mobile robot is equipped with the sensor as shown in Fig. 7. The observation data collected is the school roadway, with many trees on both sides of the road and pedestrian interference. In this algorithm test, the data collected by 0823 is used to build the map, we completed the localization of 0827 on the a priori map of 0823. Both trajectories start from the same location and eventually return to the starting point, and the trajectories are similar.

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EuRoC MAV Dataset: The experimental results of the EuRoC MAV data set are shown in Fig. 8. The EuRoC MAV data set provides the ground-truth of the trajectory. We compared the accuracy with the ORB-SLAM method. The ORB-SLAM method does not use the map information. As can be seen from the figure, compared to the ORB-SLAM method, the trajectory of proposed method is better in most of parts.

Fig. 9. Relative pose error.

In order to numerically represent the trajectory accuracy, we use RPE (Relative Pose Error) to measure the local drift of the trajectory. The calculation method of RPE is the error between ground-truth and trajectory obtained by method used. As shown in Fig. 9, the accuracy of our method’s trajectory accuracy is slightly lower than that of ORB-SLAM in both rotation and translation. YQ Dataset: The experimental result of the EuRoC MAV dataset is shown in Fig. 10 below. From the figure, we can see that proposed method can pull the trajectory back to the correct position based on the observation of the prior map after the locating jump. And from this we can conclude that our method has better robustness. Since the YQ dataset does not provide a ground-truth, we have not calculated the accuracy.

Fig. 10. The localization result of the YQ data set, the green line is the trajectory of the prior map, and the blue frame is the current localization trajectory.

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5 Conclusion We proposed a visual-inertial localization method based on multi prior visual-inertial maps and generates a summary map with a fixed map size utilizing proper compression method. For the 3D-2D data association of the map and the current query image, both geometric and reprojection constrains are used, and a final RANSAC is used to filter the inliers. In the process of map summarization, the idea of iterative map building is proposed, and the global high-precision visual-inertial map is obtained through continuous iterative optimization. To limit the summary map to a fixed size, a novel scoring strategy is exploited to compress the map, so that the global map size can converge after multiple iterations. Acknowledgement. This work was supported in part by the National Key R\&D Program of China (2017YFB1300400), and in part by the National Nature Science Foundation of China (U1609210).

References 1. Kitt B, Geiger A, Lategahn H (2010) Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme. In: IEEE intelligent vehicles symposium, vol 43, no 6, pp 486–492 2. Choi H, Yang KW, Kim E (2014) Simultaneous global localization and mapping. IEEE/ASME Trans Mechatron 19(4):1160–1170 3. Schmidt A (2014) The EKF-based visual SLAM system with relative map orientation measurements. In: Computer vision and graphics 4. Mur-Artal R, Montiel JMM, Tardos JD (2015) ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans Robot 31(5):1147–1163 5. Cieslewski T, Lynen S, Dymczyk M et al (2015) Map API - scalable decentralized map building for robots. In: IEEE international conference on robotics and automation. IEEE, pp 6241–6247 6. Dymczyk M, Lynen S, Cieslewski T et al (2015) The gist of maps - summarizing experience for lifelong localization. In: IEEE international conference on robotics and automation. IEEE, pp 2767–2773 7. Ventura J, Arth C, Reitmayr G et al (2014) Global localization from monocular SLAM on a mobile phone. IEEE Trans Visual Comput Graph 20(4):531 8. Middelberg S, Sattler T, Untzelmann O et al (2014) Scalable 6-DOF localization on mobile devices. In: European conference on computer vision, pp 268–283 9. Efron B, Hastie T, Johnstone I et al (2004) Least angle regression. Ann Stat 32(2):407–451 10. Burri M, Nikolic J, Gohl P et al (2016) The EuRoC micro aerial vehicle datasets. Int J Robot Res 35(10):1157–1163

A Deep Learning Method for Heartbeat Detection in ECG Image Zewen He1,2, Jinghao Niu1,2, Junhong Ren1, Yajun Shi3, and Wensheng Zhang1,2(&) 1

3

Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, China {hezewen2014,niujinghao2015,junhong.ren}@ia.ac.cn, [email protected] 2 University of Chinese Academy of Sciences, Beijing, China Department of Cardiology, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China [email protected]

Abstract. Although heartbeat segmentation can be done very well in ECG signals for arrhythmia detecting, there’re short of techniques for detecting heartbeat part from ECG images. We apply the powerful Faster R-CNN detector here, and achieves accurate detecting results. Along with the improved patchsampling mechanism in training, detection results are more precise. The high evaluation metric on validation data and demo of real scenes demonstrate the effectiveness of our method. Keywords: Heartbeat detection


ECG images
Faster R-CNN detector

1 Introduction ECG is a method to transform electric wave of heart to digital signals or images. Advanced techniques [12] are developed to analyze the ECGs. For example, diseases about heart like arrhythmia can be diagnosed quickly from ECG signals. Constrained by interface incompatibility, not all ECG signal data can be transferred between different hospitals. Fortunately ECG images can be shared by smartphones easily. However there lacks detection methods on ECG images. We haven’t seen related works about detecting heartbeat part from ECG images. In computer vision community, it has make huge progress on object detection, originating from the convolutional neural network (CNN). Powerful backbones [10], delicate design on loss and well-annotated datasets contribute to this success. Inspired by it, we try to prepare ECG images with labelled bounding-box for heartbeat detecting. Next we apply the Faster R-CNN detector to ECG images. Considering the scale variation of the heartbeat parts in ECG images, we propose and use customized patch-sampling mechanism in training to promote performance. Our contribution can be summarized as follows: – We apply powerful Faster R-CNN detector to ECG domains for detecting heartbeat parts, and achieve quite high accuracy. © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 356–363, 2020. https://doi.org/10.1007/978-981-32-9050-1_41

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– We propose a patch-sampling mechanism in training, leading to finer detection. – Detection performance on real ECG images are also high.

2 Related Works 2.1

Heartbeat Segmentation

Heartbeat segmentation means segmenting heartbeat intervals from ECG signal. These intervals usually contain R peak, QRS complex, as shown in Fig. 1. Digital filters are widely used for this task. Sophisticated methods based on neural networks, wavelet transform, filter banks have also been used. The details are given in the survey [12] about heartbeat classification for arrhythmia detection. It should be noted that, nearly all methods process the ECG signal but not image, and these algorithms are embedded into different devices. So only images like Fig. 1 can be shared conveniently. Our method try to detect heartbeat from the ECG images.

Fig. 1. Heartbeat segment in ECG image: The left means one heartbeat segment in ECG signal, and the right illustrates all heartbeat segments which is enclosed by colorful boxes. Our method takes charge of detecting these boxes quickly and accurately.

2.2

Object Detection

Classical Detectors. Early detection approaches were based on sliding-window, they classify the type of each sub-window seperately. Harr face detectors [2], HOG-based pedestrian detection [3], and part-based methods [4] belong to this. Although designed delicately and equipped with multi-scale strategy, performance is limited too. ConvNet Detectors. Convolution neural network (CNN)-based detectors [5–7, 13] dominates object detection community recently. With enough training, they can defeat classical methods easily on multiple benchmarks. The R-CNN and its variants [5–7] gradually promotes the upper bound of performance on two-stage detectors. In particular, Faster R-CNN [7] adopts shared backbones to proposal (RoI, namely region of interest) generation and RoI classification, resulting realtime and accurate detection. Our method is based on Faster R-CNN [7]. Details will be described in Sect. 3.2.

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3 Method 3.1

Heartbeat Detection from ECG Image

Traditional heartbeat segmentation means segmenting the part, like QRS complex, from ECG signal. Specifically in this paper, the task is localizing a box which encloses the QRS complex region, as shown in Fig. 1. Here we proposed a Faster R-CNN (Frcnn) based method to detect heartbeat part, from ECG image. Important components of Frcnn detector will be described. Then an improved training strategy will be introduced to lift detection performance on ECG image. 3.2

Faster R-CNN Detector

General Framework. The detection procedure of Frcnn starts feeding the image into Backbone Network to extract conv-feature, which is shared by two sub-networks. At the 1st stage, region proposal network (RPN) generates many RoIs based on this convfeature. Then at the 2nd stage, Fast R-CNN extracts individual feature for each RoI. It then predicts the category and refines position. Finally, post-processing techniques like soft-nms [1] will be used to remove duplicates. In the following, more details of each component and the training mechanism will be introduced.

Fig. 2. Faster R-CNN on heartbeat detection

Backbone Network. It plays a significant role in extracting meaningful features for subsequent steps like RPN and Fast R-CNN. Fortunately, the development of CNN on vision is rapid and solid. Residual Network (ResNet) [10] is the outstanding, and becomes the standard configuration in conv-net based detectors. Feature pyramid network (FPN) [11] is also proposed to detecting objects on feature of different levels. It regards the traditional ResNet as a bottom-up pathway of information, and proposes the top-down pathway and lateral connection to output multiple features with abundant details and semantics, as shown in Fig. 2. Besides, FPN detects

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objects at disjoint scale ranges at corresponding levels. We choose FPN-based ResNet here, due to its better accuracy [11] than vanilla one in detection. Head Subnet. Head predicts objects’ position and categories subsequently. As shown in Fig. 2, at the 1st stage, RPN head receives the conv-feature of entire image and predefined anchor boxes [7]. At the end here, ‘cls layer’ predicts the probability that anchor box contain object and ‘reg layer’ performs bounding-box regression [5] to refine anchor box. More specifically, ‘reg layer’ predicts 4 values to transform old RoI (here is anchor box) to new RoI, as shown in Fig. 3.

vx = (Gx − Px ) / Pw v y = (G y − Py ) / Ph vw = log(Gw / Pw ) vh = log(Gh / Ph ) Fig. 3. bbox regression: At right, the blue rectangle RoI P ¼ ðPx ; Py ; Pw ; Ph Þ is an object proposal. The 4 elements means the P’s center, width and height. And the green rectangle RoI G ¼ ðGx ; Gy ; Gw ; Gh Þ is the ground-truth bbox of object. At left, the ðvx ; vy ; vw ; vh Þ is the prediction target for ‘reg layer’, the equation describes the RoI transformation.

At 2nd stage, Fast R-CNN head adopts RoI-Align [9] to extracting same-size feature for RoIs from 1st stage. At the end, ‘cls layer’ here conducts multi-class classification for each RoI, and ‘reg layer’ outputs 4 values for RoI refinement. Network Optimization. Next we’ll introduce the loss and sample strategy in training. Loss Function. Considering Fast R-CNN, Cross-entropy loss for ‘cls layer’ is introduced for multiple classification on C categories, and smooth-L1 loss [6, 7] for ‘reg layer’ is introduced for localization. The loss function for RPN is similar, while the C need to be 2 for classifying the foreground/background. Pos/Neg RoI Sample for Training. Secondly, we need sample RoIs for training. The positive and negative ones are used for classification, while only the positive ones are for localization. For RPN and Fast R-CNN, Intersection-over-Union (IoUs) between input RoIs and ground-truth bboxes are calculated to determine pos/neg according to threshold in [7]. And the pos/neg ratio is set to 1:3 to reduce class-imbalance. The whole network is optimized by SGD, which details are in Sect. 4.1. 3.3

Patch-Based Training Scheme

Although Frcnn is strong enough for general object detection, it’s still difficult to detect heartbeat from ECG image. Firstly, the context in ECG is almost the same red grid background. If the whole image participates in training, computation capacity is wasted in processing the repeated context region. Secondly, the scale of the bbox in ECG vary greatly. As shown in Fig. 1, some bboxes of heartbeats in I-type lead are almost

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horizontal, while others in V2 are almost vertical. This makes it difficult to train detector network quickly. We turn to patch-based training for help. What Is Patch? Here patch means a region in image which contains objects. We can just crop patches from original image for training to save computation. And cropped patches only label objects whose scale is normal, while ignore the extreme ones. These patches will be used for training network like SNIPER method [13]. Patch-Based Optimization. The core technique of patch-based optimization is finding meaningful patches. Analogously to SNIPER [13], designed Greedy Patch Generation (GPG) algorithm is as shown in Fig. 4. While SNIPER uses different ranges, uniform scale range Vs is used for consistency. In training, all patches from GPG will be used as samples. Other steps in inference are same as [13].

Fig. 4. GPG Algorithm. Iorin means the original image; Borin ¼ fb1 ; . . .; bn g contains all the objects in Iorin ; Sfactors means the factors of scale; Vs means the scale range; W means the standard size of patch; S means the spatial interval of the sliding window.

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4 Experiments Experiments are performed on an ECG image dataset with only 1 category, namely heartbeat. All details about dataset, implementation and results are as follows. 4.1

Common Settings

ECG Image Dataset. Firstly, 764 ECG images are generated from real signals. The sizes are all 560 * 940. Secondly, each heartbeat is labeled by an bounding-box, as shown in Fig. 1. Thirdly, they are randomly divided into three disjoint subsets, namely train-set (252), val-set (252) and test-set (260). In addition, real-set consists of 10 images from smartphone. They will be used for demonstrating the effect on real scenes. Experimental Procedure. Firstly, hyper-parameters are searched by training detection models on train-set and evaluating on val-set. Then, detectors are trained on trainset + val-set and evaluated on the test-set. Demo results will be obtained on real-set. Implementation Details. If without specific description, following settings apply to all models. All models are implemented on the same codebase for comparison. The training and testing hyper-parameters are almost the same as Mask R-CNN [9]. The backbones and head subnets are the same as FPN [11], only plus ResNet-18-FPN. Models were trained on 4 GPUS with 1x and 2x strategy respectively. Here, 1x means that total iteration number for training is 900 and 2x means 1800, namely 29 and 59 epochs. The learning rate lr is initialized with 0.00125 * bs which is linear with minibatch size bs like [8]. For 1x strategy, lr will be reduced by 10x at 19-th, 26-th epoch; while 38-th, 52-th epoch for 2x. Frcnn Detectors are trained and tested with single scale. The scale consists of (800, 1333) and (576, 1333). For patch-based training, the scale factor set is Sfactors ¼ f2:0; 1:0; 0:5g. And the scale range is Vs ¼ ½16; 560. 4.2

Detection on Heartbeat

The evaluation is based on mAP (mean AP over multiple thresholds) from COCO. Main Results. The main results are from training models on train-set+val-set, then evaluating them on test-set. Detailed comparison is shown in Table 1. The final highest mAP is 85.6, when training with scale = (576, 1333), 1x strategy and patch-based sampling, namely GPG algorithm. Ablation Studies. They are conducted when training on train-set and evaluating on val-set. Different backbones and patch-sampling are experimented. Detailed comparison is shown in Table 1. The results shows: (1) For learning strategy, 2x always performs better than 1x; (2) scale (576, 1333) is more suitable for detection here than scale (800, 1333), due to that the ECG images are all (560, 940) here; (3) When the depth of ResNet increases, the mAP will promote; (4) Patch-based sampling is always better than original sampling strategy, under many different configurations. The (1–3) verify the effectiveness of selected hyper-parameters, and (4) demonstrate the superiority of GPG algorithm. Similar conclusion can be obtained from results on test-set.

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Table 1 mAP comparison: Left are results on test-set while right are for val-set. At 1st row, ‘M’ means method, ‘Net’ means backbone network; ‘576 + 1x’ means using 1x strategy on scale = 576. At 2nd column, ‘F’ means original Frcnn; ‘+P’ means plus patch-based optimization. At 3rd column, ‘R18’ means ResNet-18-FPN and so on. On val set On test set M Net 576 + 1x 800 + 1x 576 + 2x 800 + 2x 576 + 1x 800 + 1x 576 + 2x 800 + 2x F F F +P +P +P

R18 R50 R101 R18 R50 R101

79.7 81.6 82.2 80.9 82.4 83.1

76.7 79.3 81.2 77.6 80.9 81.8

82.3 83.4 83.6 82.9 83.9 83.8

79.8 81.3 83.1 80.6 82.2 83.6

82.7 83.8 84.7 83.8 84.5 85.1

81.1 82.6 84.2 81.2 82.5 84.9

84.2 85.2 85.4 84.8 85.4 85.6

82.6 84.2 85.3 83.2 84.9 85.5

Demo and Practical Application. It is necessary to evaluate the method on images from real world. Although there exists no annotation on real images, we can also compare methods based on their outputs. Detailed demo can seen in Fig. 5.

Fig. 5. Demo detection results in real scenes

5 Conclusion We apply the powerful Faster R-CNN detector to detecting heartbeat part in ECG images, and achieves precise results. Along with the improved patch- sampling mechanism in training, higher evaluation metric can be achieved. In addition, some demo from real scenes demonstrate the effectiveness of our method. Acknowledgement. This work was supported by the National Key R&D Program of China (No. 2016QY03D0501), by the National Natural Science Foundation of China (No. U1636220, NO. 61876183), by the Beijing Natural Science Foundation (No. 4172063).

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References 1. Bodla N, Singh B, Chellappa R, Davis LS (2017) Soft-NMS-improving object detection with one line of code. In: Proceedings of the IEEE international conference on computer vision, pp 5561–5569 2. Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vision 57(2):137– 154 3. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: International conference on computer vision & pattern recognition (CVPR 2005), vol 1. IEEE Computer Society, pp 886–893 4. Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32 (9):1627–1645 5. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580–587 6. Girshick R (2015) Fast R-CNN. In: Proceedings of the IEEE international conference on computer vision, pp 1440–1448 7. Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91–99 8. Goyal P, Dollár P, Girshick R, Noordhuis P, Wesolowski L, Kyrola A, Tulloch A, Jia Y, He K (2017) Accurate, large minibatch SGD: training imagenet in 1 hour. arXiv preprint arXiv:1706.02677 9. He K, Gkioxari G, Dollár P, Girshick R (2017) Mask R-CNN. In: 2017 IEEE international conference on Computer vision (ICCV). IEEE, pp 2980–2988 10. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778 11. Lin T-Y, Dollár P, Girshick RB, He K, Hariharan B, Belongie SJ (2017) Feature pyramid networks for object detection. In: CVPR, vol 1, p 4 12. Luz EJS, Schwartz WR, Cámara Chávez G, Menotti D (2016) ECG-based heartbeat classification for arrhythmia detection: a survey. Comput Methods Programs Biomed 127:144–164 13. Singh B, Najibi M, Davis LS (2018) SNIPER: efficient multi-scale training. In: Advances in neural information processing systems, pp 9333–9343

Multi-mode Design and Constant Current Control of Hydraulically Interconnected Energy-Regenerative Suspension Ruochen Wang , Dong Sun(&) , Renkai Ding and Xiangpeng Meng

,

School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China [email protected]

Abstract. This work develops a half-car model based on hydraulically interconnected energy-regenerative suspension (HIERS) to analyze the vehicle dynamic performances and energy-regenerative characteristics under three working modes of comfort, security and energy-feedback. Also, a multi-mode control system is proposed based on the constant current control method. Taking the road excitation frequency as the multi-mode system switching threshold, a comprehensive investigation on the vehicle dynamic performances is performed. According to the simulation results, it can be found that a 10.77% decrement of body acceleration can be reached under the comfort mode compared with comprehensiveness mode. Apart from that, tire dynamic load under security mode drops by 17.43% and theoretical energy-regenerative power under energyfeedback mode increases by 19.48%. The conclusion proves that the coordinated control of HIERS dynamic performance and energy-regenerative characteristics is achieved. Keywords: Hydraulically interconnected suspension
Energy-regenerative Multi-mode design
Switching threshold
Optimal current control




1 Introduction In the hydraulically interconnected suspension (HIS) system, different hydraulic cylinders are connected through oil pipelines and the stiffness and damping of suspension may be adjusted by force between wheels, which improves vehicle dynamic performance effectively [1]. In comparison with active suspension, HIS does not consume external energy and the cost is lower because of its simple structure. In addition, the way to recycle the energy generated by suspension vibration effectively has attracted much attention [2]. Earlier study is mainly concentrated on structure design and dynamic performance of HIS. Zhang et al. [3] designed a HIS system where the multi-body dynamic equations of multi-rigid-body hydraulic system were derived, and the influence of interconnection arrangements on dynamic performance was studied. Wang et al. [4] proposed a suspension mode switching control strategy based on fuzzy control to © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 364–372, 2020. https://doi.org/10.1007/978-981-32-9050-1_42

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achieve the optimal performance of hydraulically interconnected inertia-spring-damper suspension during the global operating conditions. The simulation and experimental results showed the effectiveness of the fuzzy switching control method. Ding [5] designed several working modes for HIS and selected different stiffness and damping coefficients for corresponding mode, which improved the dynamic performance of suspension. However, this considerable energy generated by suspension vibration is ignored and wasted. In order to regenerate the dissipated vibration energy, energy-regenerative suspensions have greatly aroused the interests of scholars all around the world. Wendel [6] proposed the model that the oil in the shock absorber was exported to drive the hydraulic motor to recycle the part of energy with reasonable control strategies. In [7], the energy-regenerative unit was added to HIS, which can recover some of vibration energy when guaranteeing the normal dynamic performance of suspension. Chen et al. [8] used the alpha method to assign weights to vehicle ride comfort, handling stability and energy-feedback power. Based on the NSGA-II algorithm, the parameters of HIERS were optimized to improve the suspension performance comprehensively. It is shown that HIRES has been studied widely. However, dynamic performance and energy-regenerative characteristics of suspension restrict each other and the coordinated control of the two is not achieved. Apart from that, the contradiction between the two dynamic performances under single mode remains outstanding. Aiming at the above problems, we establish a multi-mode control model of HIRES based on constant current control method. Three working modes, namely comfort mode, security mode and energy-feedback mode, are designed. The reasonable switching thresholds are determined. This paper is arranged as follows: In Sect. 2, HIERS model is established and the formulas of constant current control are derived according to the constant current energy-regenerative circuit. The three working modes are designed and the switching thresholds are determined in Sect. 3. And the optimal current values are also determined by simulation. In Sect. 4, the dynamic performance and energy-regenerative characteristics of HIRES are simulated on sinusoidal torsional road and D-level random road. At last, conclusions are shown in Sect. 5.

2 HIRES Model The structure of HIERS model is shown in Fig. 1. In Fig. 1, the superior chamber of left side of the dual-direction hydraulic cylinder of HIERS is connected to the inferior chamber of right side, which is highlighted in red. It is the same with the superior chamber of right side and the inferior chamber of left side is in blue. They are connected by the rectifier bridge comprising four check valves, accumulator, hydraulic motor, hydraulic pipeline and other connections.

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Z b2

mb Rectifier bridge

Check valve

Hydraulic cylinder

k1

Accumulator

M

Z w1

Z g1

Hydraulic motor

Hydraulic cylinder

k2

M

mw1

mw 2

kt 1

Constant current control

V Variable resistance

A

A E

Motor

Constant current control

kt2

Z w2 Zg2

V

E

Variable resistance Right Current I2

Left Current I1

Fig. 1. Half-car equipped with HIRES rear suspension schematic

2.1

Constant Current Control Circuit

When vehicles driving on an uneven pavement, suspensions excited by road input vibrate up and down to drive motor rotates, and the constant current energy-regenerative circuit is always in the power generation state. In case of the road excitation amplitude is small, the energy-feedback power of generator is too little to charge the super capacitor. When the excitation amplitude is large, the instantaneous power will be over-high, which may damage the circuit components. In order to recover vibration energy effectively and protect circuit components, this paper uses PI controller and adjustable resistance to establish constant current control circuit, as shown in Fig. 2. Diode

Capacity

Resistance R1

Adjustable resistance Rx Adjustable inductance

M

Motor

ib

Controller1 de/dt

ia

Controller2

Fig. 2. Constant current control circuit

In the circuit, Ib is the actual current, and Ia is the ideal current. The electrical power is given as: P0 ¼ E  Ib ¼ P2 þ Pcu

ð1Þ

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where P0 is the total power of generator, P2 is the actual output power of motor, and Pcu is the internal loss power of motor, and they are given as: 

P2 ¼ Ib2  ðR1 þ Rx Þ ¼ U  Ib Pcu ¼ Ib2  R0

ð2Þ

where R0 is the internal resistance of motor, Rx is the adjustable resistance, U is the external circuit voltage. Combining Eqs. (1) and (2) shows that: P0 P2 þ Pcu Ib2  R0 þ U  Ib U ¼ ¼ ¼ þ R0 Ib Ib2 Ib2 Ib2

ð3Þ

The adjustable resistance Rx in the constant current control circuit is given as: Rx ¼ 2.2

U  R0  R1 Ia

ð4Þ

Constant Current Control Method

In HIERS system, due to the existence of the combined structure of hydraulic motor and electrical motor, the adjustable portion of the internal damping force from hydraulic cylinder and the damping force from generator back electromotive have a mutual coupling relationship. The hydraulic motor acts the role of transmitting force and torque. P1 ¼ Pem þ Pk

ð5Þ

where P1 is the output power of hydraulic motor, Pem is the electromagnetic power of electrical motor, and Pk is the idle loss power of generator. It can be obtained when both sides of Eq. (5) are divided by the angular velocity x: T1 ¼ Tem þ Tk ¼ CT UIb þ Tk

ð6Þ

where T1 is the output mechanical torque of hydraulic motor, Tem is the output electromagnetic torque of electrical motor, Tk is the idle torque of generator, CT is torque constant, and U is magnetic flux in permanent magnet generator. According to the motor torque balance equation, T1 is expressed as: T1 ¼

PM  q g  2p gv

ð7Þ

where PM is the pressure difference, q is displacement of hydraulic motor, η is total efficiency of hydraulic motor, ηv is volumetric efficiency of hydraulic motor. According to Eqs. (2) and (3), it can be obtained that: PM  q g  ¼ CT UIb þ Tk 2p gv

ð8Þ

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Equation (4) indicates that the constant current energy-regenerative circuit designed in this paper can control the current Ib in the circuit in real time by adjusting the adjustable resistance Rx when recovering vibration energy of suspension. From Eq. (8), it can be seen that the pressure difference PM is linearly related to the current Ib. By adjusting Ib, the electrical motor speed can be controlled, thereby changing PM. The circuit current not only determines the energy-regenerative power of generator, but also affects the dynamic performance of vehicle. Therefore, the constant current control method is used in this circuit to achieve real-time control of the suspension dynamic performance and energy-regenerative characteristics.

3 Design of Working Mode 3.1

Determination of Switching Threshold

Body acceleration and tire dynamic load are two important indexes for evaluating vehicle ride comfort and handling stability. The f is the road excitation frequency, and the frequency responses of body acceleration and tire dynamic load to the excitation is shown in Fig. 3.

(a) Body acceleration

(b) Tire dynamic load

Fig. 3. Frequency response of dynamic performances

Figure 3(a) shows that 0.5–4 Hz is the body resonance frequency band. The control objective should be set as reducing body acceleration, so 0.5–4 Hz is defined as the switching threshold of comfort mode. Figure 3(b) shows that 8–12 Hz is the wheel resonance frequency band. The control objective should be set as reducing tire dynamic load, so 8–12 Hz is defined as the switching threshold of security mode. Body acceleration and tire dynamic load are both not sensitive to road input of 4–8 Hz and above 12 Hz. Therefore, the switching threshold of energy-feedback mode is defined as 4–8 Hz and above 12 Hz. Three working modes are divided as shown in Table 1.

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Table 1. Division of three working modes Frequency (Hz) 0.5 < f < 4 8 < f < 12 4 < f < 8 or f > 12

3.2

Working mode Comfort Security Energy-feedback

Evaluation index Body acceleration Tire dynamic load Energy-regenerative power

Determination of the Optimal Current Value

The left and right side currents of constant current energy-regenerative circuit affect not only the energy-feedback power, but also the dynamic performance. Thus, current values are changed to simulate and analyze the ride comfort, handling stability and energy-regenerative characteristics so that the optimal current values of each working mode can be obtained. The minimum current value is 0.3 A due to the limitations of electrical motor internal resistance and load resistance. When the value is over 5 A, the suspension performance deteriorates obviously and the sharp fluctuation of load power is not conducive to energy recovery. Therefore, the current value ranges from 0.3 A to 5.1 A, and the sampling interval is set as 0.3 A. The HIERS simulation model is established in AMESim and MATLAB/Simulink. A sinusoidal twisted road in which the amplitude is 50 mm, the excitation frequency is 1 Hz and the wheel phase difference is 180° is set as the simulation operation condition. The simulation results show that three performances indexes vary with the current value of the left and right constant current circuit, which are shown in Fig. 4.

Fig. 4. Changes of suspension performance varying with left and right side current

Figure 4 shows the changes of suspension performance varying with currents. In (a), when the current values of the left and right constant current circuit are 1.8 A and 1.8 A respectively, the body acceleration is minimal, so (1.8 A, 1.8 A) is the optimal current value for comfort mode. In (b), the tire dynamic load has the smallest value when the current values are 5.1 A and 5.1 A. So the optimal current value for security mode is (5.1 A, 5.1 A). In (c), HIRES system recycles the most energy when the current values are 4.2 A and 4.2 A, so (4.2 A, 4.2 A) is chosen as the optimal current value for energy-feedback mode. And they are applicable to all operation conditions. In order to verify the effectiveness and rationality of multi-mode control, it is necessary to obtain the optimal current value under the single control mode. Under the

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operation condition that vehicle speed is set as 10 m/s and road input is 50 mm, 1 Hz sinusoidal pavement, when left and right current value are 3.2 A and 3.2 A, body acceleration and handling stability both get the smaller value, which means the optimal comprehensive performance is achieved. Therefore, (3.2 A, 3.2 A) is defined as the optimal current value for comprehensiveness mode and it is suitable for all conditions.

4 Simulation Analysis The simulation is conducted with the vehicle driving on a sinusoidal twisted pavement at 10 m/s. The time-domain responses of body acceleration, energy-regenerative power and tire dynamic load at low frequency, medium frequency and high frequency are shown in Fig. 5. The RMS values of simulation results are shown in Fig. 6.

Fig. 5. Suspension performances on sinusoidal twisted road

Fig. 6. RMS values of suspension performances on sinusoidal twisted road

Figure 6 indicates that body acceleration becomes the largest at low frequency (f = 2 Hz), where body acceleration under comfort mode is reduced by 6.72% compared with comprehensiveness mode. The ride comfort performance of vehicle has improved. Body acceleration and tire dynamic load are both smaller at medium frequency (f = 6 Hz), where energy-regenerative power under energy-feedback mode improves by 24.35% compared with comprehensiveness mode, which means energyregenerative characteristics has greatly improved. At high frequency (f = 10 Hz), tire dynamic load is the largest, where tire dynamic load under security mode is reduced by 13.67% compared with comprehensiveness mode. The handling stability performance of vehicle has greatly improved.

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Afterwards, the vehicle is set to drive on D-level random road with a speed of 20 m/s. The three working modes of the designed HIRES are simulated and analyzed. The time-domain responses are revealed in Fig. 7. RMS values of results are listed in Table 2.

Fig. 7. Suspension performances on random road

Table 2. RMS values of suspension performances on random road Performance Body acceleration/(m s−2) Tire dynamic load/kN Energy-regenerative power/W

Comfort 1.3430 3.8365 153.79

Energy-feedback 1.5203 3.3377 236.86

Security 1.7743 2.9018 213.14

Comprehensiveness 1.5051 3.5144 198.25

It can be seen from Fig. 7 and Table 2 that body acceleration under comfort mode is reduced by 10.77% compared to comprehensiveness mode. Tire dynamic load of security mode is reduced by 17.43% compared with comprehensiveness mode. Energyfeeding power of energy-feedback mode has increased by 19.48% compared to comprehensiveness mode. The simulation results indicate that the designed HIERS with three working modes can not only take both of ride comfort and handling stability into account, but also coordinately control dynamic performance and energy-regenerative characteristics of vehicle compared with HIERS under single control mode. Above of all, HIERS with multi-mode control system can meet the requirements of obtaining optimal vehicle performance during the global operation conditions.

5 Conclusion A new type of HIRES structure with a multi-mode control system has been established. The road excitation frequency has been chosen as switching threshold and three working modes focused on comfort, security and energy-feedback are designed. Energy-regenerative unit and constant current control circuit are introduced into HIRES, and the optimal current value of each mode has been calculated by simulation. Extensive simulation results have shown that the multi-mode control system significantly enhances ride comfort, handling stability and energy-regenerative characteristic in corresponding operation condition compared with HIRES under the single control

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mode. It can be concluded that the inclusion of the multi-mode constant current control system can keep a balance between ride comfort and handling stability. The dynamic performance and energy-feedback characteristic of vehicle are also coordinately controlled.

References 1. Wang L, Xu G, Zhang N et al (2013) Experimental comparison of anti-roll bar with hydraulically interconnected suspension in articulation mode. SAE Technical Paper, SAE 2013 World Congress & Exhibition. https://doi.org/10.4271/2013-01-0710 2. Wang Y, Zhang H (2015) Research on simulation model of the vibration energy harvest for suspension of the vehicle. In: 7th international conference on intelligent human-machine systems and cybernetics, Hangzhou. IEEE Press, pp 278–281. https://doi.org/10.1109/ihmsc. 2015.86 3. Zhang N, Wang L, Du H (2014) Motion-mode energy method for vehicle dynamics analysis and control. Veh Syst Dyn 52(1):1–25. https://doi.org/10.1080/00423114.2013.847468 4. Wang R, Ye Q, Sun Z et al (2017) Study of mode switch of the hydraulically interconnected inerter-spring-damper suspension system. J Mech Eng 53(6):110–115. https://doi.org/10. 3901/jme.2017.06.110 (in Chinese) 5. Ding F, Han X, Luo Z et al (2012) Modelling and characteristic analysis of tri-axle trucks with hydraulically interconnected suspensions. Veh Syst Dyn 50(12):1877–1904. https://doi.org/ 10.1080/00423114.2012.699074 6. Wendel G, Stecklein G (1991) A regenerative active suspension system. SAE Technical Paper, International Congress & Exposition. https://doi.org/10.4271/910659 7. Zou J, Guo X, Xu L et al (2018) Simulation research of a hydraulic interconnected suspension based on a hydraulic energy regenerative shock absorber. SAE Technical Paper, WCX World Congress Experience. https://doi.org/10.4271/2018-01-0582 8. Long C, Zhang C, Wang R et al (2017) Modeling and optimization design of hydraulically interconnected energy-regenerative suspension. Trans Chin Soc Agric Mach 48(1):303–308. https://doi.org/10.6041/j.issn.1000-1298.2017.01.040 (in Chinese)

A Temperature Control Method for Car Room Based on Single User Personalized Comfort Nan Ye1, Lin-hua Zhuang2,3, and Ning Li1(&) 1

2

Shanghai Jiao Tong University, Shanghai 200240, China [email protected] Shanghai New Energy Automotive Air Conditioning Engineering Technology Research Center, Shanghai 201180, China 3 Songz Automobile Air Conditioning CO., LTD., Refrigeration Research Institute, Shanghai 201108, China

Abstract. In this paper, a car room temperature control method based on single user personalized comfort is proposed: Firstly, a smartphone application based on android platform is designed to collect user’s information and their personalized comfort dataset are established. Secondly, the DENFIS (Dynamic Evolving Neural-Fuzzy Inference System) method is used. This method helps to extract the fuzzy inference system and obtain the control targets according the dataset. Finally, MATLAB is used to verify the method. The result shows that the control target obtained by this method basically meets the needs of the user. Keywords: Personalized thermal comfort Cab temperature control


Fuzzy inference


1 Introduction At present, the widely used vehicle room temperature control method is the method which combines artificial set value and feedback control mode. In this kind of control mode, in order to form a comfortable environment, the driver needs to interact with the HVAC system frequently. Thus, problems such as determining the comfortable temperature and comfortable air flow rate of different drivers and reducing the frequency of user’s interaction with the HVAC system should be taken into consideration. And this needs to form an intelligent temperature control method. This paper will focus on producing such an intelligent temperature control method. The study of human thermal comfortable has a long history. Prof. Fanger of Denmark proposed the PMV (Predicted Mean Vote) index in 1970 [1]. He divided the human body’s thermal sensation into 7 different grades, ranging from −3 to +3, which corresponding to seven different thermal states of the human body from cold to hot. In that model, the PMV is calculated by using four environmental variables: air temperature, relative humidity, air flow rate and average radiant temperature, as well as two human factors: thermal resistance of clothing and human metabolic rate, as shown in Fig. 1. Some follow-up scholars also carried out various researches on this basis. MacArthur gave indoor regulation schemes of air conditioning with PMV as the control target [2]; Calvino designed a fuzzy PID-controlled HVAC system based on © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 373–381, 2020. https://doi.org/10.1007/978-981-32-9050-1_43

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PMV [3]. However, the data collection of thermal resistance and human metabolic rate is difficult, therefore the practicability of this model is bad. And some studies indicate that there is a serious deviation between the index and the actual user’s thermal experience [5]. Therefore, people turned to find reasonable methods on collecting thermal comfort data. Murakami et al. used the Internet web interface on the PC to obtain user data [7]; Jazizadeh et al. obtained user data based on the smartphone platform [9].

Fig. 1. PMV model

Fig. 2. Temperature control flow chart of vehicle room based on user’s comfort

The driver’s main activity inside the car and their suits are almost the same in the same season. Therefore, the collected thermal comfort data of the individual has good stability and practicability. In the method proposed in this paper, users upload personal information and their own evaluation of the cabin environment through the corresponding application based on smart phone [8]. The information provided by users composes the personal comfort data. And according to the dataset, the host computer establishes the corresponding fuzzy inference system through the adaptively adjusted DENFIS (Dynamic Evolutionary Neuro-Fuzzy Inference System) method [10]. The user’s comfortable temperature and comfortable air flow rate are then sent to the controller to achieve related control. The specific process is shown in Fig. 2. The method described in this paper has the following characteristics: Firstly, a new method of temperature control in the vehicle compartment is proposed. It is different from the temperature control method combined with the artificial set value and the feedback control method. The required set value by the user is obtained from the established data set. Secondly, the data set will update in each interaction and the

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corresponding comfort fuzzy model will also be corrected. After multiple corrections, it can accurately produce the user’s comfortable temperature and comfortable air flow rate.

Fig. 3. User interaction interface

Fig. 4. DENFIS method

2 Method Introduction 2.1

Data Collection

The comfort of the driver in the cabin is mainly determined by two aspects: cabin temperature and air flow rate. The demand for them of different drivers is varied in different seasons. As stated in the introduction, it is necessary to establish an intelligent control method. To this end, personal dataset about the demand of vehicle room is needed [10]. One important aspect is choosing the medium which users use to submit information. With the continuous development and popularization of mobile devices, this paper chose android development platform to form the information collecting terminal [4]. Users can use the related application on the smart phone to upload a series of feedback information. Another important aspect is the user’s evaluation of the environment. A widely accepted solution is the user’s thermal comfort scale, which quantifies the user’s assessment of the environment temperature. ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers) classifies users’ environmental characteristics into seven level: cold, slightly cold, neutral, slightly hot, hot, corresponding to seven intervals of −3 to +3. Based on the above research results, this paper uses the sensation scale to quantify the user’s evaluation of the cabin environment. The user interaction interface is shown in Fig. 3. It is mainly composed of three parts, the first part is seasonal information, the second part is the user’s evaluation of the temperature, and the third part is the user’s evaluation of the air flow rate. The corresponding sliding bar can be used to reflect the

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user’s perception of the cabin temperature and the air flow rate. And the relevant data is transmitted to the terminal to establish a corresponding data set to provide data support for the subsequent control scheme. 2.2

Data Set Initialization

According to the user’s information, an approximate temperature comfort data set and air flow rate comfort data set can be formulated. Taking the data prediction ability and processing time and other factors into account, each data set consists of 100 data points. The user’s subsequent information feedback will be input as a new data point, replacing the earliest data point in the data set. Over time, the corresponding personalized data set will be formed after multiple interactions.

(a)Data of user1

(b)Data of user2

(c)Data of user3 Fig. 5. Temperature comfort data set for users

Different people have different requirements for the cabin environment. Figure 5 shows three different temperature comfort state data sets in the summer. The horizontal axis is the user’s evaluation of the cabin temperature, that is, the TPI (Thermal Preference Index). It varies from −3 to +3 representing the cold to hot evaluation of the ambient temperature; the vertical axis is the corresponding temperature setting of the HVAC. Figure 6 shows two different air flow rate comfort state data sets in the summer when the cabin ambient temperature is comfortable. The horizontal axis is the user’s

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evaluation value of the air flow rate, that is, the AFPI (Air-Flow Preference Index). It varies from −3 to +3 representing the low to high evaluation of the air flow rate; the vertical axis is the corresponding air flow rate setting of the HVAC.

(a)Data of user1

(b)Data of user2

Fig. 6. Air flow rate comfort data set for users

2.3

Personalized Comfort Model

The temperature control method for the vehicle room proposed in this paper can make the model learn the user’s preference for the vehicle room environment when user give enough feedback information. That is, as the number of interactions increases and the data set becomes more and more huge, the model can predict the users’ preference for the temperature of the vehicle room and the air flow rate. This means that users can achieve a comfortable vehicle environment through less interactions. To this end, an adaptively adjusted DENFIS method is chosen [6]. The method uses the ECM (Evolving Clustering Method) to cluster the data. And based on the clustering results, the fuzzy set and fuzzy rules are constructed to further generate the fuzzy inference system. The method flow is shown in Fig. 4. ECM is a clustering method based on the Euclidean distance of a vector. For a vector x, y in any two European spaces, the distance is P kx  yk ¼

q i¼1

jxi  yi j2

1=2

q1=2

; x; y 2 Rq

ð1Þ

The clustering algorithm of ECM is as follows: Step 0: When it comes the first input data x1 , the first cluster C1 is constructed, and the cluster radius R1 ¼ 0. Step 1: If all the input data has been processed, the algorithm ends and the clusters and cluster centers at this time are output. Otherwise, calculate the Euclidean dis tance Dij ¼ xi  Oj , j = 1, 2,


, n, where xi is the current input data and Oj is one of the previously generated n cluster centers.

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Step 2: For the current input data xi , if there is any distance value Dik satisfying Dik  Rk , where k takes all the values of j that can make Dij  Rj . And set Dil ¼ minðDik Þ, so this data belongs to the cluster Cl . At this time, no update is needed for the remaining clusters, and the algorithm goes back to step 1. Otherwise, the algorithm proceeds to step 3. Step 3: For the current input data xi , if there is no Dik satisfying Dik  Rk . Then, for each cluster Cj in the whole n clusters, calculate Sij ¼ Dij þ Rj , where j = 1, 2,


, n.   And set Sia ¼ min Sij . If Sia [ 2  Dthr , where Dthr is the cluster radius threshold defined by users. This means that the current input data xi does not belong to any existing cluster. Thus a new cluster should be recreated according to the method in step 0, and then the algorithm goes back to step 1. Otherwise, the cluster center Oa of the cluster Ca should be updated, and the cluster radius Ra should be increased, and then returned to step 1. By using the above-mentioned ECM to obtain clustering sets, fuzzy sets and fuzzy rules can be further established. The fuzzy rules are expressed as follows: If x 2 R1 ; then y ¼ f1 ð xÞ If x 2 R2 ; then y ¼ f2 ð xÞ . . .. . . If x 2 Rm ; then y ¼ fm ð xÞ The Ri in the fuzzy rule, where i = 1, 2,


, m, is one of the different fuzzy sets. It is determined by the fuzzy membership function corresponding to Eq. (2).

lðxÞ ¼ mf ðx; a; b; cÞ ¼

8 0 > > < xa

ba

cx > > : cb 0

xa axb bxc cx

ð2Þ

Where b is the clustering center of the input space, a ¼ b  d  Dthr , c ¼ b þ d  Dthr , d
½1:2; 2, Dthr is the cluster radius threshold defined by users. The first-order Takagi-Sugeno model is used in the fuzzy rule fi ð xÞ, y ¼ f i ð x Þ ¼ a0 þ a1  x

ð3Þ

Where the parameters a0 and a1 can be generated by the linear least square method for the p groups of data in the fuzzy set x 2 ½a; c. For the input value x0 , the output of the inference system is the weighted average of the output of each fuzzy rule: y0 ¼

Pm w f ð x0 Þ i¼1 Pm i i i¼1 wi

ð4Þ

Where wi is the weight of the i-th fuzzy rule. And for the input value x0 the value is wi ¼ lRi ðx0 Þ.

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3 Method Analysis and Verification In this paper, the input value of the fuzzy inference system is the user’s evaluation value of the vehicle environment, namely the value of TPI and the value of AFPI. The output value is the corresponding cabin temperature (°C) and the air flow rate (%). For the following data sets shown in Fig. 7, the models are shown in Fig. 8 by using MATLAB with the above method. According to the above fuzzy rule, the output of the fuzzy inference system by using the first-order Takagi-Sugeno model are obtained, as shown in Fig. 7. Thus the user’s comfortable temperature range and the comfortable air flow rate range can be obtained, as shown in Tables 1 and 2.

(a)Temperature fuzzy inference system

(b)Air flow rate fuzzy inference system

Fig. 7. Fuzzy inference system

(a)Temperature fuzzy membership function

(b)Air flow rate fuzzy membership function

Fig. 8. Fuzzy membership function

As shown in the table, the user’s comfortable temperature interval is [23.1 °C, 24.3 °C], the comfortable air flow rate is [39.3%, 44.9%]. In the actual control process, due to the unbalanced distribution of the temperature and its slowly variation, the above temperature comfort interval can be appropriately widened. And there is a coupling relationship between the air flow rate and the temperature of the air outlet. In the case of a certain amount of cooling capacity, the excessive air flow rate will result in a high air

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outlet temperature, and the insufficient air flow rate will result in a low air outlet temperature. Therefore, while controlling the air flow rate, it is also necessary to ensure that the outlet temperature is within an acceptable range.

Table 1. Comfortable temperature interval table. TPI −0.5 0 0.5

Temperature (°C) 23.1 23.8 24.3

Table 2. Comfortable air flow rate interval table AFPI −0.5 0 0.5

Air flow rate (%) 39.3 42.2 44.9

4 Conclusion This paper proposes a temperature control method for car room based on single user personalized comfort. The method collects driver comfort data and establishes a data set through an application. And we build a model by using an adaptively adjusted DENFIS method, through which the user’s comfortable temperature and comfortable air flow rate can be obtained. The method can learn the user’s thermal comfort preference, and surpasses the conventional temperature control mode which combining artificial set value and feedback control mode. It can reduce the interaction between the user and the HVAC. Combining with the subsequent feedback control, it can achieve an Intelligent control of the cabin temperature.

References 1. VanHoof J (2008) Forty years of Fanger’s model of thermal comfort: comfort for all? Indoor Air 18(3):182–201 2. McArthur A (1987) Thermal interaction between animal and microclimate: a comprehensive model. J Theoret Biol 126(2):203–238 3. Calvino F et al (2010) Comparing different control strategies for indoor thermal comfort aimed at the evaluation of the energy cost of building. Appl Therm Eng 30(16):2386–2395 4. Javed M, Li N, Li S (2017) Personalized thermal comfort modeling based on support vector classification. In: 2017 36th Chinese control conference (CCC), Dalian, China, pp 10446– 10451 5. Doherty T, Arens EA (1988) Evaluation of the Physiological bases of thermal comfort models. ASHRAE Trans 94:1371–1385 6. Kasabov NK, Song Q (2002) DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Trans Fuzzy Syst 10(2):144–154 7. Murakami Y et al (2007) Field experiments on energy consumption and thermal comfort in the office environment controlled by occupants’ requirements from PC Terminal. Build Environ 42(12):4022–4027

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8. Xu Y, Chen S, Javed M, Li N, Gan Z (2018) A multi-occupants’ comfort-driven and energyefficient control strategy of VAV system based on learned thermal comfort profiles. Sci Technol Built Environ 24(10):1141–1149 9. Jazizadeh F et al (2011) Continuous sensing of occupant perception of indoor ambient factors. In: ASCE international workshop on computing in civil engineering 10. Chen S, Li N, Li S (2015) Learning personalized thermal comfort profile of HVAC system based on DENFIS. In: 26th Chinese process control conference, Nanchang, China

Three-Dimensional Trajectory Optimization Design of Parafoil System Obstacle Avoidance Based on Switched System Method Qiaodan Liu1 and Xiang Wu1,2(&) 1

School of Mathematical Sciences, Guizhou Normal University, Guiyang 550001, China [email protected] 2 School of Electrical Engineering, Southeast University, Nanjing 210096, China

Abstract. We research on a problem for three-dimensional trajectory optimization design of parafoil systems obstacle avoidance. Since open-loop control lacks of robustness in practical engineering applications, to ensure the robustness of system, by devising a new piecewise state feedback controller, we convert previous problem to the closed-loop switched systems optimal control problem with state inequality restrictions. Due to the unknown switching instants and the existence of such restrictions, we have difficulty in handling this problem by employing classical optimization methods. To address faced dilemma, time-scaling transformation and smoothed penalty approach are applied. Finally, an unconstrained parameter optimization problem is derived, and an improved conjugate gradient algorithm is introduced for addressing this problem. Simulation experiment indicates proposed algorithm is valid, and a desired three-dimensional trajectory of parafoil is obtained and avoids terrain obstacles to reach the landing point more precisely. Keywords: Parafoil system
State-feedback controller Optimal control
Trajectory optimization


Switched systems


1 Introduction The parafoil research can date back to 1970 [1]. In the past years, parafoil system is developed in various domains, such as Unmanned Aerial Vehicle [2], the airdrop system [3], obstacle avoidance [4] and so on. For these applications, parafoil systems obstacle avoidance problems are extensively researched in this article. One account is that parafoil inevitably encounters unexpected obstacles during the parafoil system homing. For example, severe terrain, bad weather, and so on. Switched systems are one of relatively significant hybrid system. It contains some discrete or continuous active subsystems and a switching law which causes switch among different subsystems to achieve satisfied performance [5, 6]. Due to the discrete trait of switching signal, it is not applicable to deal with the switched systems optimal control problem by applying classical optimization approaches. To solve this problem, maximum principle [7] and dynamic programming method [8] have been extended to © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 382–392, 2020. https://doi.org/10.1007/978-981-32-9050-1_44

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this problem. However, due to the complexity of practical problems, it is difficult to calculate optimal numerical solutions by using these two methods. In recent years, several new computational methods are available. Xu and others [9] proposed two stage optimization method. Original problem is separated from two optimization problems to calculate respectively. But this method only gets the sub-optimal solution based on fixed switching sequence. For unknown switching sequence, Zhai [10] obtained the optimal or sub-optimal solution by constructing embedded systems. Differ from the above methods, and this article has two contributions to be presented. We first define a new piecewise state feedback controller to make the switching sequence pre-fixed. Thus, pervious problem can be converted to a closed-loop switched systems optimal control problem with state inequality restrictions. By applying timescaling transformation and smoothed penalty method, such restrictions are merged into the previous cost function of this problem, deriving an unconstrained parameter optimization problem. We then put forward a novel improved conjugate gradient algorithm. Besides an example three-dimensional trajectory design of parafoil system terrain obstacles avoidance is handled to illustrate the feasibility of this algorithm.

2 Problem Formulation 2.1

Unpowered Parafoil System

Motion equation of three-degree-freedom unpowered parafoil system can be expressed by 8 x_ 1 > > < x_ 2 x_ > > : 3 x_ 4

¼ vf cos x3 þ vw ¼ vf sin x3 ; ¼u ¼ vd

ð1Þ

with initial conditions x1 ðt0 Þ ¼ x0 ; x2 ðt0 Þ ¼ y0 ; x3 ðt0 Þ ¼ z0 ; x4 ðt0 Þ ¼ h0 ;

ð2Þ

where x1 , x2 and x4 denote corresponding coordinate of parafoil under geodetic coordinate system, vd ; vf and vw represent the velocity of vertical fall, horizontal flight and wind. Turn angle of parafoil is x3 , and u 2 U  R4 refer to the control input. Note XðtÞ ¼ ½x1 ðtÞ; x2 ðtÞ; x3 ðtÞ; x4 ðtÞT 2 R4 and _ XðtÞ ¼ FðXðtÞ; uðtÞÞ; Xðt0 Þ ¼ x0 ;

ð3Þ

where x0 ¼ ½x0 ; y0 ; z0 ; h0 T . 2.2

Problem Formulation

Assumption 1. g1 ðXðtÞÞ and g2 ðXðtÞÞ are twice continuously differentiable function. The following several constraints are set up before the problem formulation:

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Firstly, the landing point requires approach to the target landing point. Then, we assume that the terminal constraints are described by x1 ðtF Þ ¼ x1F ; x2 ðtF Þ ¼ y2F ; x4 ðtF Þ ¼ 0;

ð4Þ

where ðx1F ; y2F Þ represents the coordinate of landing point, terminal time is tF ¼ z0 =vd . Secondly, the landing takes form of obeying the wind direction to protect the load. Thus, we assume the terminal wheel angle satisfies the following equality constraint: cosðx3 ðtF ÞÞ ¼ 1:

ð5Þ

Thirdly, electrical machinery only provides limited energy. So the control input satisfies the following control constraint: juj  umax ;

ð6Þ

where umax is admissible maximum control variables, that is minimum turning radius. Finally, homing trajectory of parafoil does not directly collide with the obstacles. Thus, there has the following path restriction: g1 ðXðtÞÞ ¼ mðx1 ðtÞ; x2 ðtÞÞ  x4 ðtÞ  0;

ð7Þ

where x4 ðtÞ ¼ x4 ð0Þ  vz t; mðx1 ; x2 Þ refers to the equivalent obstacle altitude at the coordinate described by ðx1 ; x2 Þ. The following three-dimensional trajectory optimization design problem of parafoil system obstacles avoidance is presented: Problem 1. Rendered a parafoil system (1) with the initial condition (2), determine the control input u 2 U to minimize following cost function Z tf ^J ¼ u2 dt; ð8Þ t0

and subject to state inequality restrictions (7). 2.3

Terrain Obstacle Model

Note different terrain hinders the parafoil in different degrees. This paper chooses independent peaks as obstacles. As shown in Fig. 1, the highest altitude of the peaks is 3700 m, and the coordinate of ground are (900,500) in the center of the peaks.

Fig. 1. Photograph of peaks

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3 Problem Transformation 3.1

Problem 1 Transformation

We assume that the piecewise state feedback controller is selected by: uðtÞ ¼

hX þ1 s¼1

ps XðtÞn½ts1 ;ts Þ ðtÞ;

ð9Þ

where ts ðs ¼ 1; 2;


; hÞ are switching times, and satisfies 0 ¼ t0 \t1 \t2 \


\ th \th þ 1 ¼ T. Feedback gain matrixes is p ¼ ½p1 ;


; ph þ 1 T ; p 2 Rh þ 1 and the set of including such p is denoted by P, the characteristic function is n½ts1 ;ts Þ ðtÞ and satisfies ( n½ts1 ;ts Þ ðtÞ ¼

1;

t 2 ½ts1 ; ts Þ

0;

t 62 ½ts1 ; ts Þ

:

By replacing selected controller (9) into the nonlinear system (3), we derive _ s ðXðtÞ; pÞ; t 2 ½ts1 ; ts Þ; s ¼ 1;


; h þ 1; XðtÞ ¼F

ð10Þ

 Xð0Þ ¼ x0 ;

ð11Þ

s ðXðtÞ; pÞ ¼ FðXðtÞ; ps XðtÞÞ. where F The control restriction depicted by (6) and cost function depicted by (8) become   hX  þ1   g2 ðXðtÞÞ ¼  ps XðtÞn½ts1 ;ts Þ ðtÞ  umax  0; ð12Þ  s¼1  J ¼

Z 0

T

hX þ1 s¼1

!2 ps XðtÞ

n½ts1 ;ts Þ ðtÞdt:

ð13Þ

Then, Problem 1 is converted to the closed-loop switched system optimal control problem. Note that this problem possesses decision variables to be selected, including the optimal switching instants ts and feedback gain matrixes p. But we have difficulty in handling the above issue through traditional optimization methods. Next, time-scaling transformation is employed to address this dilemma. 3.2

Time-Scaling Transformation

A function given by tðsÞ : ½0; h þ 1 ! R is defined and differential equation is given by

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t_ðsÞ ¼

hX þ1 s¼1

qs n½s1;sÞ ðsÞ;

ð14Þ

tð0Þ ¼ 0;

ð15Þ

where 0  qs ¼ ss  ss1  T, and that is switching instants. Provided a k with satisfying k  ½0; h þ 1, nk ðsÞ is given by the following characteristic function: ( nk ðsÞ ¼

1; s 2 k; 0; s 62 k:

ð16Þ

Therefore, time-scaling transformation is made up such transformation depicted by (14)–(15). Let q ¼ ½q1 ;


; qh T , and the set of including such qs is denoted by Q. Then, according to integrating (16), for any s 2 ½s  1; sÞ; s ¼ 1;


; h þ 1, we can achieve t ð sÞ ¼

s1 X

qr þ qs ðs  s þ 1Þ:

ð17Þ

r ¼1

For per s 2 ½s  1; sÞ; s ¼ 1;


; h þ 1, we yield the following form: tðsÞ ¼

s X

qr ¼

r ¼1

s X

ðsr  sr1 Þ ¼ ss ;

ð18Þ

r ¼1

especially, tðh þ 1Þ ¼ sh þ 1 ¼ T.

_

_

s ðXðtÞ; pÞ, through For any s 2 ½s  1; sÞ; s ¼ 1;


; h þ 1, let F s ðX ðsÞ; q; pÞ ¼ qs F applying (14), (15) to (4), (5), (7) and (10)–(13) respectively, we have _ _ _ _ X ðsÞ ¼ F s ðX ðsÞ; q; pÞ; s 2 ½s  1; sÞ; s ¼ 1;


; h þ 1; _

X ð0Þ ¼ x0 ;

ð20Þ

gn ðX ðsÞÞ  0; s 2 ½s  1; sÞ; s ¼ 1;


; h þ 1; n ¼ 1; 2;

ð21Þ

_

_

_

_

_

x1 ðsh þ 1 Þ ¼ x1F ; x2 ðsh þ 1 Þ ¼ y2F ; x4 ðsh þ 1 Þ ¼ 0; cosðx3 ðsh þ 1 ÞÞ ¼ 1; _

J ðq; pÞ ¼

hX þ1 Z s s ¼1

_

ð19Þ

s1

h _ i2 qs ps ðX ðsÞÞ ds;

ð22Þ ð23Þ

where gn ðX ðsÞÞ ¼ qs gn ðXðsÞÞ. Therefore, Problem 1 is converted to the following closed-loop switched systems optimal control Problem 2:

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Problem 2. Rendered a switched dynamic system (19) with initial condition (20), determine ðq; pÞ 2 Q  P to minimize the cost function given by (23), and subject to inequality restrictions (21) and equality restrictions (22). 3.3

Smoothed-Penalty Function Method

Because an inequality constraint given by (21) is equal to countless restrictions, we have difficulty in addressing Problem 2 through traditional optimization approaches. The smoothed-penalty function method is applied to handle such constraints in this part. Define Z Gs;n ðq; pÞ ¼

s

s1

n _ o max gn ðX ðsÞÞ; 0 ds; s ¼ 1; 2;


; h þ 1; n ¼ 1; 2:

ð24Þ

Note that the formulation depicted by (24) can be equivalently written by Gs;n ðq; pÞ ¼ 0; s ¼ 1;


; h þ 1; n ¼ 1; 2:

ð25Þ

n _ o Since max gn ðX ðsÞÞ; 0 is discontinuous function. Next, a new smoothing function described by (26) is introduced to approximate the inequality constraint (21). pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi wa ðxÞ ¼ 0:5ð x2 þ 4a2 þ xÞ;

ð26Þ

where a [ 0 and it is enough small constant. For any x 2 R and a [ 0, wa ðxÞ exists two properties as follows: lim wa ðxÞ ¼ maxfx; 0g;

ð27Þ

0\wa ðxÞ  maxfx; 0g  a:

ð28Þ

a!0 þ

Under the property described by (27), the formulation given by (25) becomes Z Gs;n ðq; p; aÞ ¼

s

s1

 _  wa gn ðX ðsÞÞ ds ¼ 0; s ¼ 1; 2;


; h þ 1; n ¼ 1; 2:

ð29Þ

Then, the above constraints given by (29) are merged into the function (23) by employing penalty function approach, yielding the following augmented cost function: ~Ja;l ðq; pÞ ¼ uðx_3 ðsh þ 1 ÞÞ þ

hX þ1 Z s s ¼1

s1

_

Ls ðX ðsÞ; q; pÞds;

ð30Þ

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where

 _ 2   _   _  _ Ls ðX ðsÞ; q; pÞ ¼ qs ps ðX ðsÞÞ þ l wa g1 ðX ðsÞÞ þ wa g2 ðX ðsÞÞ ,

_

and

_

uðx3 ðsh þ 1 ÞÞ ¼ cosðx3 ðsh þ 1 ÞÞ þ 1, l [ 0 is called penalty parameter. Thus, Problem 2 is converted to the following unconstrained optimal parameter selection problem. Problem 3. Rendered nonlinear switched dynamic system (19) with initial condition (20), determine ðq; pÞ 2 Q  P to minimize the cost function depicted by (30).

4 An Improved Conjugate Gradient Algorithm 4.1

Gradient Formulate

The following Hamiltonian function is defined: _

_

_

_

Hs ðX ðsÞ; q; p; k; Þ ¼ Ls ðX ðsÞ; q; pÞ þ ðks ðsÞÞT F s ðX ðsÞ; q; pÞ;

ð31Þ

where kðsÞ denotes co-state vector and satisfied equations are as follows: 

k_ s ðsÞ

T



. _ _ ¼ @Hs ðX ðsÞ; q; p; ks ðsÞÞ @X ðsÞ; s 2 ½s  1; sÞ;

. T _ _ kh þ 1 ðh þ 1Þ ¼ @uðx 3 ðsh þ 1 ÞÞ @x 3 ðsh þ 1 Þ:

ð32Þ ð33Þ

Since Problem 3 can be handled by employing gradient algorithm, next, the gradient formula of (30) is presented by Theorem 1 to solve Problem 3. Theorem 1. For each s 2 ½s  1; sÞ; s ¼ 1;


; h þ 1, the gradient formula of the cost function depicted by (30) related to qs and ps separately, are presented by  @ ~Ja;l ðq; pÞ @qs ¼  @ ~Ja;l ðq; pÞ @ps ¼

Z

s



.  _ @Hs ðX ðsÞ; q; p; ks ðsÞÞ @qs ds;

ð34Þ



.  _ @Hs ðX ðsÞ; q; p; ks ðsÞÞ @ps ds:

ð35Þ

s1

Z

s

s1

_ _ Proof. According to (19) and (31), integrate ðks ðsÞÞT X ðsÞ partially, we obtain _

~Ja;l ðq; pÞ ¼ uðx_3 ðsh þ 1 ÞÞ  ðkh þ 1 ðh þ 1ÞÞT _x ðh þ 1Þ þ ðk0 ð0ÞÞT X ð0Þ þ hX þ1 Z s _ _ ðHs ðX ðsÞ; q; p; ks ðsÞÞ þ ðk_ s ðsÞÞT X ðsÞÞds: s ¼1

s1

ð36Þ

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By calculating the first order variation of (36), we achieve . _ _ _ _ d~Ja;l ðq; pÞ ¼ ð@uðx3 ðsh þ 1 ÞÞ @x3 ðsh þ 1 ÞÞdx3 ðsh þ 1 Þ  ðkh þ 1 ðh þ 1ÞÞT dxðh þ 1Þ þ h . _  _ hP þ1 R _ _ s @Hs ðX ðsÞ; q; p; ks ðsÞÞ @X ðsÞ dX ðsÞ þ ðk_ s ðsÞÞT dX ðsÞ þ s1 s ¼1 .  .  i   _ _ @Hs ðX ðsÞ; q; p; ks ðsÞÞ @qs dqs þ @Hs ðX ðsÞ; q; p; ks ðsÞÞ @ps dps ds: ð37Þ The terms are collected in (37). And combining (32) with (33) yields hP þ1 R

d~Ja;l ðq; pÞ ¼

s¼1

hP þ1 R s¼1

s s1



s s1



.  _ @Hs ðX ðsÞ; q; p; ks ðsÞÞ @qs dqs ds þ

.  _ @Hs ðX ðsÞ; q; p; ks ðsÞÞ @ps dps ds:

ð38Þ

Theorem 1 have been proven completely. 4.2

An Improved Conjugate Gradient Algorithm

Let r ¼ ðq; pÞ, r0 ¼ ðq0 ; p0 Þ 2 Q  P represents initial point. Next, the following improved conjugate gradient algorithm is introduced to address Problem 3: Step.1: Choose r0 ¼ ðq0 ; p0 Þ 2 R2h þ 1 , set e [ 0 and 0\d\0:5, q1 is a given integer and d\q2  1, there exist a constant c [ 0, such that c\1 is constant and c [ c. ( b~k

¼

    g~k þ 1 ~ g~k ; b~k ; if ~g~Tk ~g~k þ 1   c ~ 0; else;

.  .   2   g~k ~ b~k ¼ ~g~Tk þ 1 g~~k þ 1  ~g~Tk þ 1 ~g~k  ~ g~k ~ g~Tk d~k ; ~g~ ¼ rJ~a;l ðr~ Þ; d~ ¼ ~g~ : k

k

k

ð39Þ

ð40Þ

k

where ~k :¼ 0; k
k refers to Euclidean distance. Step.2: If k~g0 k  e, then stop and set r ¼ r0 , else continue Step.3. Step.3: Calculate step length depicted by a~k [ 0, inspect two conditions as follows: T ~Ja;l ðr~ þ a~ d~ Þ  ~Ja;l ðr~ Þ þ da~ ~ k k k k k g~k d~k ;

ð41Þ

q1 d~kT ~g~k  d~kT ~g~k þ 1   q2 d~kT ~ g~k :

ð42Þ

Step.4: Let r~k þ 1 ¼ r~k þ a~k d~k , if ~g~k þ 1  e is satisfied, then stop and set r ¼ r~k þ 1 , else continue Step.5.

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    Step.5: If g~Tk ~g~k þ 1  [ c ~g~k þ 1 g~k , then set d~k þ 1 ¼ ~ g~k þ 1 and ~k ¼ ~k þ 1, return Step.3, otherwise continue Step.6. Step.6: Calculate b~k and d~k þ 1 from (39) to (40) respectively, set d~ ¼  ~g~ þ b~ d~ , and ~k ¼ ~k þ 1, continue Step.3. kþ1

kþ1

k k

According to the above algorithms, the optimal solution is r ¼ ðq ; p Þ.

5 Numerical Results The basic motion parameters of parafoil system are described by: vf ¼ 18 m/s, vw ¼ 5 m/s, vd ¼ 6 m/s; umax ¼ vf =Rmin ¼ 0:18; a ¼ 0:025; l ¼ 1; where R is turning radius. Suppose starting point is (1500, 2000, 3900) and landing point is (−1000, −1000, 0) behind the obstacle. In addition, we suppose that initial time 4 P is t0 ¼ 0 and terminal time is t4 ¼ tF ¼ 3900=6 ¼ 650 s. And uðtÞ ¼ ps XðtÞ s¼1

v½ts1 ; ts Þ ðtÞ is selected as piecewise state feedback controller, where switching times are ts ; s ¼ 1; 2; 3. By applying the algorithm described by Subsect. 4.2, we solve Problem 3 by using Matlab 2015b. Three optimal switching times are presented by: t1 ¼ 291:6443; t2 ¼ 401:0229; t3 ¼ 517:7948; and the feedback gain matrixes are presented by p1 ¼ ½7:1030; 20:3088; 0:2557; 29:8324; p2 ¼ ½4:4566; 26:7398; 0:3788; 33:4247; p3 ¼ ½26:7857; 20:0893; 0:2679; 17:8571; p4 ¼ ½23:5294; 16:4705; 1:1765; 5:4118: To compare with the existing approach, we solve Problem 3 by using the open-loop control method proposed by [11]. Comparative consequences are given in Fig. 2 and Table 1. We can find that the computation time of the algorithm described by Subsect. 4.2 saves 47.65%. And the optimal value deeply less than 3171.115 obtained by using the method given in [11]. By making full use of state feedback message, Fig. 2 confirms that our method can continuously adjust trajectory to reduce the error of turning radius. Thus, the optimal three-dimensional trajectory of parafoil system avoids terrain obstacles and reaches the landing point more precisely. Table 1. Comparisons of the results obtained by the algorithms in this paper and the approach given in [11] Algorithm Final cost Computation time (s) Algorithm 4.2 1219.6596 214.8750 Reference [11] 3171.1150 622.2193

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Fig. 2. The three-dimensional optimal trajectory of parafoil obtained by using our method and the method given in [11]

6 Conclusion This article investigates a three-dimensional trajectory planning problem for parafoil system obstacle avoidance, which is converted to a closed-loop switched system optimal control problem with state constraint. By time-scaling transformation and smoothed-penalty function method, an unconstrained parameter optimization problem is derived. A novel improved gradient algorithm is used to achieve optimal value. Simulation result shows proposed approach makes the landing accuracy enhanced and satisfies given state inequality restrictions than the existing open-loop control method. Acknowledgments. All writers sincerely convey their thanks to the editor. The National Natural Science Foundation of China supported this article under Grant no. 61563011.

References 1. Menard G, Nicolaides J, Speelman R (1970) A review of para-foil applications. J Aircr 7 (5):423–431 2. Sohail M, Leow W, Won S (2018) Non-orthogonal multiple access for unmanned aerial vehicle assisted communication. IEEE Access 6:22716–22727 3. Cacan M, Costello M (2018) Adaptive control of precision guided airdrop systems with highly uncertain dynamics. J Guid Control Dyn 41(5):1–11 4. Sun H, Sun Q, Luo S (2018) In-flight compound homing methodology of parafoil delivery systems under multiple constraints. Aerosp Sci Technol 79:85–104 5. Li X, Cao J, Perc M (2018) Switching laws design for stability of finite and infinite delayed switched systems with stable and unstable modes. IEEE Access 6:6677–6691 6. Kamgarpour M, Tomlin C (2012) On optimal control of non-autonomous switched systems with a fixed mode sequence. Automatica 48:1177–1181 7. Ye H, Xu H (2016) Global stabilization for ball-and-beam systems via state and partial state feedback. J Ind Manag Optim 12(1):17–29 8. Borrelli F, Baotic M, Bemporad A (2005) Dynamic programming for constrained optimal control of discrete-time linear hybrid systems. Automatica 41(10):1709–1721

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9. Xu X, Antsaklis J (2002) Optimal control of switched systems via nonlinear optimization based on direct differentiations of value functions. Int J Control 75:1406–1426 10. Zhai J, Niu T, Ye J (2017) Optimal control of nonlinear switched system with mixed constraints and its parallel optimization algorithm. Nonlinear Anal Hybrid Syst 25:21–40 11. Wu X, Zhang K, Cheng M (2018) Optimal control of bioprocess systems using hybrid numerical optimization algorithms. Optimization 67:1287–1306

Design of Automatic Illumination Culture System for Haematococcus pluvialis Based on LED Shigang Cui, Xinqi Li(&), Yongli Zhang, Xingli Wu, and Lin He Tianjin University of Technology and Education, Tianjin, China [email protected]

Abstract. In this paper, LED illumination technology was used to design a suitable illumination system for Haematococcus pluvialis culture. The system used STM32 microprocessor as the main control chip and white LED with red and blue peaks as the supplementary light source to ensure the appropriate light ratio for the multiplication and induction stage of Haematococcus pluvialis. Using STM32 MCU to output PWM signal, the LED driving circuit was designed to control the current of the driving circuit, so as to realize the linear adjustment of light intensity. The automatic dimming function based on dissolved oxygen in algae solution was realized by programming. The experimental results show that the system can automatically and linearly adjust the light intensity. The maximum intensity of the system can meet the cultivation requirements, and the uniformity of the light is great. Keywords: Automatic dimming system


Haematococcus pluvialis
LED

1 Introduction Astaxanthin, a carotid, is a strong natural antioxidant and widely used in health products and cosmetics [1, 2]. Haematococcus pluvialis is recognized as the best natural Astaxanthin-producing organism. But because of its harsh cultivation conditions, the yield is low and the price is expensive. The culture of Haematococcus pluvialis can be divided into two stages [3]. The first stage is cell proliferation culture stage, which requires 1000–1500 lx light conditions with red light quality as the main light quality. In the second stage of assistantship induction, 10 000–20 000 lx light with blue light quality was needed in this stage [4]. The illumination intensity of the two stages is quite different, so the system is designed to automatically adjust the illumination according to the actual growth of algae liquid.

2 System Overall Design The system adopts a modular design, including a detection module, a control module, a display module, a fill light module, and a power module. The system selects STM32 as the control module, and outputs the PWM signal with adjustable duty cycle to complete © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 393–400, 2020. https://doi.org/10.1007/978-981-32-9050-1_45

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the system dimming function; The fill light module is composed of a driving circuit and a plurality of LED light bars; The detection module is composed of an OD detector and a light intensity detection sensor to realize a data detection function; The display module is composed of a touch screen and has a good human-computer interaction interface. The block diagram of the system is shown in Fig. 1.

Fig. 1. System composition block diagram.

3 System Hardware Design 3.1

Selection of LED Light Source

Due to the red-light quality required in the value-added culture stage during the culture process of Haematococcus pluvialis and the blue light quality in the induction stage, the Os-ram white LED lamp bead with red and blue double peaks is selected as the light source. Os-ram is one of the world’s most innovative lighting companies and has many world-leading patents. It is one of the world’s two largest light source manufacturers. The LED lamp bead has the characteristics of small heat generation, high power and large illumination intensity, and is suitable for the illumination requirements of the system. Its spectrum is shown in Fig. 2.

Fig. 2. LED spectrometer.

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In order to find a more convenient way to arrange the lamp bead with better uniformity of illumination, we replace the traditional lamp board with the package form of the light bar. It is more convenient to change the spacing and number of lamp beads and seek the best arrangement of uniformity. The exterior of the lamp strip is designed with cold gel encapsulation and has good sealing effect. According to the length and length of the culture frame, different numbers of 3 cm equidistant light bars are arranged. 3.2

Driving Circuit Design

It only lights up when there is a forward current through the LED, so we designed the PWM driver circuit. PWM uses the pulse width modulation signal to repeat the ON/OFF driver to achieve the purpose of adjusting the average LED current. The schematic diagram of the PWM dimming driver circuit is shown in Fig. 3.

Fig. 3. Principle diagram of PWM dimming driving circuit.

A PWM signal source is required for PWM dimming [5]. The driver turns on and off the gate of the FET according to the high and low levels, and repeatedly switches the LED driver. When the switching frequency is higher than 100 Hz, the human eye cannot see the turn-off of the LED. The human eye has a detention effect on the image. The turn-on and turn-off time are averaged, and only the brightness changed by duty cycle can be observed. Therefore, the system only needs to provide digital pulses with different width. This system changes the brightness of LED lamp by changing the pulse width of constant current source. When the signal source transmits data to the single chip microcomputer, the signal needs to be amplified twice by two amplifiers to obtain the signal frequency that the single chip can collect, thereby realizing the control effect of the single chip on the LED signal source. 3.3

Design of Detection Module

The system detection module is mainly composed of a DO detector and a light intensity sensor. The DO detector is mainly used to detect the oxygen content of the algae liquid to obtain the growth condition of the algae cells, and the light intensity sensor is mainly used for detecting and recording the light intensity of the current culture frame. The system uses the GY-30 digital module to detect the light intensity which connects SDA and SCL with the PB6 and PB7 pins of the MCU, and connects the

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address pin ADDR directly to the ground. The schematic diagram of the light intensity detection module is shown in Fig. 4.

Fig. 4. Schematic diagram of illumination intensity detection circuit.

The system uses the CITY 4OXV sensor to detect the oxygen content of the algae liquid. The circuit schematic is shown in Fig. 5. The OD detector amplifies the signal through operational amplifier circuit, and then eliminates the interference by filtering the signal by connecting two capacitors in series in the feedback loop and output loop. Finally, the OD detector is transmitted to the AD interface of the single chip computer for digital-to-analog conversion to obtain the oxygen content value.

Fig. 5. Schematic diagram of oxygen content detection circuit.

4 System Software Design 4.1

Construction of OneNET Display Platform

OneNET acts as a Paas layer in the Internet of Things, and builds a bridge between the SaaS layer and the IaaS layer, providing intermediate core capabilities to upstream and downstream [6]. The testing process mainly includes user registration and login, creation of new products and connection platform. The test process is shown in Fig. 6.

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Plaorm domain

Create product

TCP connecon 183.230.40.39/876

Create trigger

Create device

EDP device connecon Product ID + device number

Create applicaon

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Fig. 6. Test flow chart.

Change the EDP service address domain name and port number to the initial settings, using the OneNET account, device name and authentication information as login parameters. Organize the EDP connection message by using the corresponding interface in the SDK, send it to the platform, and establish an EDP connection with the platform. The code is as follows:

EdpPacket*send_pkg=PacketConnect2(PID,AUTHINFO); int ret=DoSend9(sockfd,send_pkg->_data,send_pkg->_write_pos); DeleteBuffer(&send_pkg); Using the interface functions provided in the DSK, write code to transfer the oxygen content detection and light intensity detection data to the platform. The code is as follows. cJSON*json_data=cJSON_Createobject(); cJSON*AddNumberToObject(json_data,"DO",26); cJSON*AddNumberToObject(json_data,"Lux",1800); EdpPacket*send_pkg=PacketSavedateJson(NULL,json_data,kTypeSimpleJson WithoutTime,0); int ret=DeSend(sockfd,send_pkg->date,send_pkg->write_pos); DeleteBuffer(&send_pkg); cJSON_Delete(json_data)

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Programming Design

For the ATM32 microcontroller, three PWM signals can be generated by the timer for external devices. The timer T/C1 generates two PWM signals through the registers OCR1A and OCR1B and the output units OC1A and OC1B, which are respectively output from the two pins PB1 and PB2; The timer T/C2 itself can be configured to control the LED current through the PB3 output of the PWM signal. The PWM adjustment accuracy is 8 bits, the upper limit of the counter is 255, the initial value of OCR2 is k, the system clock frequency is f, and the division factor is M. The frequency is calculated as: fPWM ¼

8M ; 256

D¼

k 255

ð1Þ

After the system is powered on, first initialize each module, and then repeatedly scan and read the oxygen content detection data to determine the growth state of the algae liquid. By adjusting the PWM duty cycle by comparing the setting intervals, the brightness of the light source is changed to achieve the function of automatically adjusting the light intensity. At the same time, the algae oxygen content monitoring data and the light intensity detection data are uploaded to the cloud platform to realize the remote monitoring display data function.

5 Results and Analysis 5.1

Analysis of Light Intensity Distribution and Uniformity

Design experiments to verify the uniformity of the LED strips of the system to ensure uniform illumination of the algae. The optical quantum flux density is measured at 25 cm below the light source. The measurement area is 40 cm  40 cm. The distribution of the optical quantum flux density is shown in Fig. 7.

Fig. 7. Distribution of Optical Quantum Flux Density at 100 W Power.

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Figure 7 shows that the closer the distribution of optical quantum flux density is to the center point, the higher the density of optical quantum flux is, the lower the measured value is. The uniformity requires that the mean square deviation be as small as possible. The experimental results show that at 100 W power, the uniformity of the optical quantum flux density is 0.85, the mean value is 100 lmol/(m2
sÞ, and the variance is 75. The maximum illumination intensity can reach 2000 lx, and the uniformity coefficient is more than 0.8, which can reach higher illumination intensity and better uniformity. 5.2

System Overall Testing

700 ml algae solution was placed in the system for 20 days, and shaded with shade curtain on the outside to prevent light loss. Microscopic examination showed that algae cells accumulated rapidly and assistantship-containing algae cells appeared on the 18th day. The system can meet the design requirements and provide suitable light source for the cultivation of Haematococcus pluvialis (Fig. 8).

(a)1day

(b) 9day

(c)18day

Fig. 8. Microscopic image of algae liquid. Acknowledgments. This work is supported by National Key Research Program under Grant 2017YFB0403904.

References 1. Córdova P, Baeza M, Cifuentes V, Alcaíno J (2018) Microbiological synthesis of carotenoids: pathways and regulation. IntechOpen, 26 September 2018 2. Rani Juneius CE, Sundari M, Eswaralakshmi R, Elumalai S (2018) Seaweed liquid fertilizers: a novel strategy for the biofortification of vegetables and crops. Springer, Singapore, 21 June 2018 3. Razon LF (2011) Net energy calculations for production of biodiesel and biogas from haematococcus pluvialis and nannochloropsis sp. Springer, Japan, 15 June 2011 4. Cheng C-H, Bai Y-W (2012) Using fuzzy logic and light-sensor for automatic adjustment of backlight brightness in a mobile computer. In: 2012 IEEE 16th international symposium on consumer electronics (ISCE), 04 June 2012

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5. Hu R (2018) The status quo and prospect of model-free adaptive control. In: Proceedings of 2018 7th international conference on advanced materials and computer science (ICAMCS 2018). International Information and Engineering Association: Computer Science and Electronic Technology International Society, p 4 6. Yanshu N (2018) Single phase sine wave PWM inverter circuit simulation and the design of filter based on matlab. In: Proceedings of 2018 2nd international conference on electronic information technology and computer engineering (EITCE 2018), 4 p

Study on pH Control of Haematococcus pluvialis Solution Based on Neural Network Controller Shigang Cui, Yunqi Huang(&), Lin He, Yongli Zhang, and Xingli Wu Tianjin University of Technology and Education, Tianjin, China [email protected]

Abstract. This paper mainly studies the control method of pH value in Haematococcus pluvialis algae solution. Aiming at the problems of large overshoot, difficulty in setting control parameters, long adjustment time and poor antiinterference ability of conventional PID in the process of pH control, a method combining BP-NN (back propagation neural network) algorithm with PID control is proposed. This method makes use of the complex non-linear mapping ability and strong learning ability of BP-NN to achieve precise control of the control system. In order to verify this method can enhance the control effect of the system, a mathematical model of the pH value of Haematococcus pluvialis algae solution was established, and the simulation research and analysis were carried out by using Matlab. The simulation results show that the BP-NN PID controller designed in this paper has better stability and robustness to the pH control of algae solution pH control, and the control quality has obvious advantages. Keywords: Haematococcus pluvialis


pH
BP neural network

1 Introduction Astaxanthin is a carotenoid with high antioxidant activity, which is widely used in aquaculture, chemical industry, pharmaceuticals, food and cosmetics industries [1, 2]. Natural astaxanthin is mainly extracted from crustaceans, Phaffia yeast and Haematococcus pluvialis. Because of the highest content of astaxanthin in Haematococcus pluvialis, it is recognized as the best biological source of astaxanthin production in nature. At present, more than 10 companies in five countries have achieved commercial cultivation of Haematococcus pluvialis, but their annual production of astaxanthin is less than 10 tons [3]. With the increasing market demand, its market price per kilogram remains high. However, the specific mechanism of astaxanthin accumulation induced by Haematococcus pluvialis is still unclear, which leads to the difficulty of large-scale cultivation of natural astaxanthin by Haematococcus pluvialis. Therefore, finding a way to promote the efficient induction and accumulation of astaxanthin by Haematococcus pluvialis has become a research hotspot at home and abroad. PH is a key environmental factor in the culture process of Haematococcus pluvialis. Green swarmer are especially sensitive to the pH of culture environment during the © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 401–407, 2020. https://doi.org/10.1007/978-981-32-9050-1_46

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growth stage of Haematococcus pluvialis. However, during the growth of Haematococcus pluvialis, the pH of algae solution will gradually increase with the selective consumption of nutrients in the medium, and then inhibit the growth of algae in the later stage [4]. Although the principle of PID control is simple and easy to use, it is ineffective to control the complex process of non-linearity, time-varying, coupling, parameter and structure uncertainty. The pH of Haematococcus pluvialis solution is a complex multi-variable coupling object, the PID controller is hard to achieve better control effect for the system with complex control process. In this paper, the pH value of algae liquid in the culture system of Haematococcus pluvialis is taken as the research object, and the best PID control parameters can be found by using the strong non-linear approximation ability of BP neural network, and adjusts them in real time, so as to improve the control quality.

2 Traditional PID Control Principle In modern industrial control, the most common control is deviation-based PID controller. According to the preset expected value r(t) and the actual output value y(t), the deviation e(t) is formed. eð t Þ ¼ r ð t Þ  yð t Þ

ð1Þ

The deviation is used as feedback input, and the parameters of the PID controller are continuously modified to meet the control requirements. The control principle is as follows (Fig. 1):

Fig. 1. PID control schematic.

When the pH value of Haematococcus pluvialis algae solution is controlled by the traditional PID controller, because of its non-linearity and large lag, there is a delay phenomenon in the control process, and it is hard to achieve precise control.

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3 Improvement of BP Neural Network Algorithms on Traditional PID 3.1

BP-NN Structure

BP-NN has complex non-linear mapping ability and strong ability learning ability, so that the controlled object can achieve the effect of the set value with the fastest speed and stability. The control principle is as follows (Fig. 2):

Fig. 2. BP-NN control schematic.

The BP-NN designed in this article consists of input layer, output layer and a hidden layer, and the structure is shown in the following figure. The three nodes in the input layer are preset value r, actual output value y and deviation e. According to the empirical formula, four hidden layer nodes are set up in this paper. KP, KI and KD of PID controller represent three neurons in the output layer. wij and wjk are weight coefficients of hidden layer and output layer respectively (Fig. 3).

Fig. 3. Three-layer BP-NN schematic.

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BP-NN Structure

The input of the input layer is: ð1Þ

Oi

¼ xð i Þ

i = 1; 2; 3

ð2Þ

The input and output of the hidden layer are: 8 m P ð2Þ ð1Þ > < netið2Þ ðtÞ ¼ Wij Oi i1   > : Oð2Þ ðtÞ ¼ f netð2Þ ðtÞ j i

j ¼ 1; 2; 3

ð3Þ

The activation function of neurons in the hidden layer is: f ðzÞ ¼ tanhðzÞ ¼

ez  ez ez þ ez

The input and output of the output layer are: 8 q P > ð3Þ ð3Þ ð2Þ > Wjk Oj > netk ðtÞ ¼ > > > j¼0 >   > > ð3Þ ð3Þ < Ok ðtÞ ¼ g netk ðtÞ > Oð3Þ ðtÞ ¼ KP > > 1 > > ð3Þ > O > 2 ðtÞ ¼ KI > > : ð3Þ O3 ðtÞ ¼ KD

k ¼ 1; 2; 3

ð4Þ

ð5Þ

The activation function of neurons in the output layer is: 1 ez gðzÞ ¼ ð1 þ tanhðzÞÞ ¼ z 2 e þ ez

ð6Þ

In formula (2) to formula (6), the upper corner label (1) delegates the input layer, the upper corner label (2) delegates the hidden layer, and the upper corner label (3) delegates (3) the output layer. w(2) ij and wjk are the weight coefficient of the hidden layer and the output layer. KP, KI and KD are three adjustable control parameters of PID. 3.3

Backward Algorithm

When training the weights, weights are usually corrected in the direction of error reduction. As the number of learning increases, the deviation e(t) becomes smaller and smaller. The performance index function is [5, 6]: 1 EðtÞ ¼ ½r ðtÞ  yðtÞ2 2

ð7Þ

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By using the steepest descent method to modify weights, the variation of weight coefficients of the output layer is: ð3Þ

Dwkj ðtÞ ¼ b

@E ðtÞ ð3Þ @wkj

ð3Þ

þ kDwkj ðt  1Þ

ð8Þ

In the formula, t is the number of learning times, b is the learning rate (0 < b < 1), and k is the momentum factor (0 < k < 1). In practical application, k is generally taken from 0.1 to 0.8. From formulas (1) and (5): 8 @DuðtÞ ¼ eðtÞ  eðt  1Þ > > > Oð13Þ ðtÞ > < @DuðtÞ ¼ eð t Þ ð 3Þ O2 ðtÞ > > @Du ð t Þ > > : Oð33Þ ðtÞ ¼ eðtÞ  2eðt  1Þ þ eðt  2Þ

ð9Þ

In summary, the formula for calculating the weights of the output layer are: 8 ð2Þ ð3Þ ð2Þ < wð2Þ ðt Þ jk ðt Þ ¼ @Dwjk ðt 1Þ þ bdk O j  ð3Þ @yðtÞ @DuðtÞ 0 : dk ¼ eðtÞsgn @DuðtÞ
ð3Þ g netkð3Þ ðtÞ

ð10Þ

@Ok ðtÞ

In the formula, d(3) k is a local gradient, indicating the changes required for the weights. Similarly, the formula for calculating the weight coefficient of hidden layer in BPNN are: 8 ð2Þ ð2Þ ð3Þ ð2Þ > < wij ðtÞ ¼ @Dwij ðt  1Þ þ bdj Oi ðtÞ h iP 3 ð3Þ ð2Þ ð3Þ ð3Þ 0 > dk wjk : dj ¼ f netk ðtÞ

ð11Þ

k¼1

In formula (10) and formula (11): g0 ðzÞ ¼ gðzÞ½1  gðzÞ

ð12Þ

 1 1  f 2 ðzÞ 2

ð13Þ

f 0 ðzÞ ¼

4 Simulation and Analysis For the sake of proving the correctness and applicability of the proposed method, this paper first establishes a mathematical model for the pH value of Haematococcus pluvialis algae solution, and then simulates and analyses it by using Matlab. The simulation model built by Simulink is shown in the following figure (Fig. 4):

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Fig. 4. Simulation model of BP-NN PID controller.

According to the actual measurement data and control experience, the transfer function of pH control process is set as follows: G ðsÞ ¼

2:57 e20:6s 46:7s þ 1

ð14Þ

In traditional PID controller, the control parameters KP = 0.78, KI = 0.2, KD = 0.3. In the BP-NN PID controller, the initial weight coefficients between layers are wij and wjk. They take random numbers on the interval [−0.5, 0.5], b = 0.3, k = 0.01. The simulation curves of step response of traditional PID controller and the BP-NN PID controller are shown in Fig. 5. It can be seen from the simulation results that the effect of the BP-NN PID controller has been significantly improved, the transition time is short, there is no overshoot, and the error curve is more smooth and stable.

Fig. 5. Simulation results of PID control and BP-NN PID control.

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5 Conclusion The pH of Haematococcus pluvialis solution is a multi-variable coupling object. When it is controlled by traditional PID control strategy, the desired effect cannot be achieved. In this paper, a new BP-NN PID controller is proposed, which combines BP-NN algorithm with PID. The complex non-linear mapping ability and strong learning ability of BP-NN are utilized. For the sake of proving the correctness and applicability of the proposed method, this paper first establishes a mathematical model for the pH value of Haematococcus pluvialis algae solution, and then simulates and analyses it by using Matlab. The simulation results show that, compared with traditional PID control, BP-NN PID controller has superior control performance, short transition time, less overshoot, better accuracy and robustness, and can achieve ideal control effect. Acknowledgment. This work is supported by National Key Research and Development Program of China 2017YFB0403904.

References 1. Ren X, Chen L, Li R, Liu T (2019) Selectively extract astaxanthin from wet biomass of Haematococcus pluvialis. Chin J Process Eng 19(01):136–143 (in Chinese) 2. Zhang C, Liu J, Zhang L (2017) Cell cycles and proliferation patterns in Haematococcus pluvialis. Chin J Oceanol Limnol 35(05):1205–1211 3. Choochote W (2015) Effect of Nitrogen and Phosphorus on growth and astaxathin production of haematococcus pluvialis. In: Abstracts of the 13th international symposium on biocontrol and biotechnology 4. Yang J, Peng X, Chao Y (2017) Reliability prediction of power communication network based on BP neural network optimized by genetic algorithm. In: 2nd international conference on computational modeling, simulation and applied mathematics 5. Zhang Y, Su H (2018) PID controller parameter adjustment based on BP neural network. J Nankai Univ 51(03):26–30 (in Chinese) 6. Liu D (2018) Speed control and simulation of wind turbine based on BP neural network controller. Instrum Tech (06):13–16 (in Chinese)

An Autoencoder-Based Dimensionality Reduction Algorithm for Intelligent Clustering of Mineral Deposit Data Yan Li1,2,3, Xiong Luo1,2,3(&), Maojian Chen1,2,3, Yueqin Zhu4, and Yang Gao5 1 School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China [email protected] 2 Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China 3 Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China 4 Development and Research Center, China Geological Survey, Beijing 100037, China 5 China Information Technology Security Evaluation Center, Beijing 100085, China

Abstract. Currently, there has been a dramatic growth of data size and data dimension in geophysics, while achieving the rapid advancements of geological big data technologies with the support of various detection methods. Then, highdimensional and massive geological data impose very challenging obstacles to traditional data analysis approaches. Given the success of deep learning methods and techniques in big data analysis applications, it is expected that they are also able to achieve the satisfactory performance in dealing with high-dimensional complex geological data. Hence, through the combination of one of the effective implementations of deep learning, i.e., autoencoder, and a clustering algorithm, i.e., K-means, in this paper we achieve the dimensionality reduction for complex data, so as to extract useful data features from mineral deposit data, with the purpose of improving computational efficiency. The experimental results demonstrate the effectiveness our developed method. Keywords: High-dimensional data
Dimensionality reduction
Autoencoder
K-means algorithm

1 Introduction The rapid advancements of various exploration techniques have made the geological big data grow exponentially. Generally speaking, they are produced in basic geology, mineral geology, hydrogeology, and many other kinds of spatio-temporal remote sensing observation activities in geophysics [1]. Actually, geological big data is characterized by a high-dimensional and massive model. Additionally, there may be redundant or irrelevant data in those data, which makes it inconvenient to process and © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 408–415, 2020. https://doi.org/10.1007/978-981-32-9050-1_47

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store the high-dimensional data. Hence, the distribution of high-dimensional data brings new challenges to effectively discover the characteristics and mine useful information from high-dimensional geological big data. An effective way to address these issues is dimensionality reduction, which means to map the data from high-order space to low-dimensional space through linear or nonlinear methods. In this context, various methods of dimensionality reduction have been proposed to serve many purposes, such as pattern classification [2] and information visualization [3]. Among the available approaches, principal component analysis (PCA) is the most commonly used linear dimension reduction technique [4]. It projects the original data to its main direction with maximum variance, without considering any data relations and class labels. Furthermore, linear discriminant analysis (LDA) is a supervisory method for finding linear subspaces, which can discriminate the best among different types of data [5]. Although some progresses have been made in this direction, it still cannot fully match the demands of capturing useful features of high-dimensional data when only using a simple learning model, e.g., PCA, due to the complexity of a geological big data system [6]. We need to develop a more effective model to learn relationships between data and hiding information, through the use of some advanced computational intelligence methods, e.g., deep learning algorithm. Currently, there are few works related to the dimensionality reduction for mineral deposit data, while employing advanced deep learning algorithms. Deep learning as a powerful methodology has achieved some satisfactory performance in dealing with a wide range of data analysis issues in academic and industrial communities [7–9]. Among the implementations of deep learning, autoencoder is a special technique of constructing neural network (NN) [10]. Its output and input are the same. Actually, the traditional autoencoder is a symmetric NN with only one hidden layer, where the input layer has the same number of neurons as the output layer, and the hidden layer has fewer neurons than the input layer. In recent years, due to its ability of effectively improving the classification accuracy of data, autoencoder has witnessed a growing interest in data mining research for various fields [11]. Considering the limitations of those traditional dimensionality reduction techniques mentioned above and the advantages of autoencoder, in this paper we propose an autoencoder-based dimensionality reduction algorithm to effectively mine the characteristics of mineral deposit data and discover the relationship between geological elements, enabling us to have a more intuitive understanding and observation for geological big data. Specifically, in response to the possible loss of data information due to dimensionality reduction, a clustering algorithm, i.e., K-means, is also incorporated to achieve intelligent clustering of mineral deposit data.

2 Backgrounds 2.1

Autoencoder

Autoencoder is an unsupervised NN model used to learn efficient data characteristics [10, 12], which is composed of two parts. It can learn the internal features of the input,

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which is called encoding. Then, it can reconstruct the original input data with the internal features learned, which is called decoding. Usually, autoencoder is used for dimensionality reduction. The structure of a basic autoencoder is shown in Fig. 1, which includes three layers totally. The first is input layer, the second is hidden layer, and the third is output layer.

Fig. 1. The structure of an autoencoder.

Firstly, the autoencoder encodes the input data X to get the new feature Z. Let x 2 Rn ¼ X be the input data, and z 2 Rp ¼ F be the new feature. The encoding process is z ¼ f ðWx þ bÞ, where f is an activation function (e.g., sigmoid function or tanh function), W is a matrix weight, and b is a bias vector. It can be found here that the encoding process consists of a linear combination and a nonlinear activation function. Then, the autoencoder maps the new features z to the reconstruction x’ with the same shape as the input data x. The decoding process is x0 ¼ f 0 ðW0 x þ b0 Þ, where f′, W′, and b′ used for the decoding have the same meaning with the corresponding f, W, and b used for the encoding, respectively. In general, f and f′ are the same activation function. In order to let the reconstructed data x′ to be as consistent as possible with the input data x, we train this model with a loss function. This function is to minimize the difference between x and x′, and it is called reconstruction error L defined by: Lðx; x0 Þ ¼

N X ðx  x0 Þ2 i¼1

N

;

ð1Þ

where N is the number of nodes in the input layer. Generally, the dimension of the feature space F is smaller than that of the input space X , so the feature vector z can be considered as a compressed representation of the input data x. However, it has been found that when there is a large difference of dimensions between the original data and the required compression data, using only one hidden layer of autoencoder will generate a large error. Hence, with the popular use of deep NN, an autoencoder with multiple hidden layers has attracted much attention, since it can capture more important information and learn richer representations of the input data. The structure of a multiple-autoencoder is in Fig. 2.

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Fig. 2. The structure of a multiple-autoencoder.

2.2

K-means

K-means is a widely used algorithm for clustering [13]. Its main idea is to divide a given sample points set, into K clusters according to the distance between sample points, which makes the points in the cluster as close as possible and the distance between the clusters as large as possible. Let X = {x1, x2, …, xn} be a set of samples to be clustered, where sample xi (i = 1, 2, …, n) is a d-dimensional vector. Here, X will be clustered into a set of K clusters, i.e., C = {c1, c2, …, ck} (k  n). The process of finding cluster partition C is as follows. (1) The cluster center {l1, l2, …, lk} is initialized by randomly selecting K samples from the sample set X. (2) The distance between the sample xi and each cluster center lj is calculated through: dij ¼ arg min j

n X k  X   x i  l j 2 : i¼1 j¼1

2

ð2Þ

Then, the sample is allocated to its nearest cluster center. (3) The new cluster center lj for all sample .  points in cluster cj (j = 1, 2, …, k) is P  cj  . recalculated through lj ¼ x2cj x (4) The above steps are repeated, until K cluster centers are no longer changed.

3 The Autoencoder-Based Dimensionality Reduction Algorithm for Mineral Deposit Data A good dimensionality reduction is critical to achieving satisfactory analysis performance for mineral deposit data. However, as mentioned above, considering the highdimensional and massive characteristics of mineral deposit data, it is difficult to meet this requirement by using some traditional approaches. Therefore, through the combination of autoencoder and K-means, we achieve the dimensionality reduction for

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complex mineral deposit data, so as to extract useful data features from it, with the purpose of improving computational efficiency. Here, the autoencoder-based dimensionality reduction algorithm for mineral deposit data includes two parts. (1) For the mineral deposit dataset, autoencoder is used here to learn its implicit features and obtain the low-dimensional representation of data. The encoding process of autoencoder is a process of data dimensionality reduction, and the output of each hidden layer is another representation of the input. (2) The K-means algorithm is also applied to reduce the dimensional data. In order to facilitate the evaluation of clustering results and to analyze the reasonable dimensionality of mineral deposit attribute reduction, we take the mineral deposit type as a category label, where each category contains 60 mineral deposits in accordance with the preprocessing operation.

4 Experimental Results and Discussion 4.1

Experimental Dataset and Configuration

For the purpose of comparing the impact of different dimensions on clustering results after dimensionality reduction, we select three types of mineral deposit data and eliminate redundant data and blank values. Specifically, since the original mineral deposit data is a text dataset, the dataset should be vectorized before clustering, with the purpose of facilitating the implementation of our autoencoder-based dimensionality reduction algorithm. Then, the mineral deposit data are reduced to different dimensions through the autoencoder. Additionally, the K-means algorithm is used to cluster each low-dimensional dataset, and the clustering results are compared through the accuracy rate and recall rate, which defined in the following subsection. Generally, a mineral deposit has many attributes, such as metallogenic ages, sedimentary structures, rock formations, and some others. These attributes describe and define a particular type of mineral deposit. In this experiment, we select 3 different deposit categories from the deposit dataset, namely contact metasomatism deposits, volcanic hydrothermal deposits, and sedimentary layered deposits. Each category contains 5 attribute combinations, including rock series, genetic type, geotectonic environment, lithology code, and formation age. Each mineral deposit attribute is converted into a 50-dimensional vector. Then, those vectors of 5 attributes are joined as a vector of the mineral deposit data, while obtaining a 250-dimensional mineral deposit data vector. After preprocessing the data by moving redundant data and blank missing data, 60 mineral deposits are selected for each category finally. Then, a mineral deposit dataset containing 180 records is obtained. Hence, in this experiment, the dataset is with a size of 250  180. Here, the format of mineral deposit data is shown in Table 1. Meanwhile, by vectorizing those original mineral deposit text data, an example of the resulting word vector is also provided in Table 2.

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Table 1. The format of mineral deposit data Rock series

Genetic type

Calc alkalic series Tholeiitic series Shoshonite series

Crust-mantle mixed source Mantle source Crust source

Geotectonic environment Graben basin

Lithologic code Adamellite

Passive margin

Grained syenogranite Granite diorite

Graben basin

Formation age Upper jurassic Upper cretaceous Lower cretaceous

Table 2. An example of word vectors Mineral deposit attributes Calc alkalic series Crust source Adamellite Upper jurassic Dioritic porphyrite

Word vectors 0.002516828 0.0025052794 0.030185107 … −0.0104168365 0.010410899 −0.026155483 … −0.009546665 -0.014511113 −0.020231554 … 0.006219968 −0.002484169 −0.0034684755 … 0.006961098 0.00446491 −0.014926236 …

Finally, our experiments are conducted on the Pycharm 2018.1.1 environment running on an Inter(R) Core(TM) i7-8550U, 2.00GHZ, 8.00 GB RAM Computer. 4.2

Metrics

In this experiment, we evaluate the results of intelligent clustering on three metrics, including accuracy rate, recall rate, and running time. For the dataset, the accuracy is calculated as Accuracy = TP/N, where TP is the true positive and N is the total number of samples in the dataset. For one of three deposit categories, recall is calculated as Recall = TP/n, where n is the total number of samples in this category. 4.3

Experimental Results

According to the algorithm proposed in Sect. 3, we use an autoencoder model with 8 hidden layers to achieve the dimensionality reduction, where the first 4 hidden layers are used for encoding. Through the encoding process of this model, the vector of 250 dimensions is reduced to 2, 3, 4, 5, and 6 dimensions, respectively. In Fig. 3a, we provide a relationship between clustering accuracy and dimension, which shows that the accuracy increases as the dimension increases and it increases quickly in dimensions 2 and 3. After dimension 3, the accuracy still increases, but the rate of increase decreases. Figure 3b shows the running time for different dimensions. With the increase of data dimensions, the calculation effort is reasonable and the running speed also increases. Table 3 shows the recall rate of clustering. With the increase of dimensions, the overall trend of recall rate increases, but it decreases for some categories.

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(b) Computational time

Fig. 3. Computational accuracy and computational time for different dimensions.

Table 3. Comparison of recall rate for different dimensions Deposit category Contact metasomatic Volcanic hydrothermal Sedimentary

2-D 0.516 0.566 0.583

3-D 0.633 0.850 0.450

4-D 0.879 0.478 0.521

5-D 0.766 0.716 0.650

6-D 0.816 0.683 0.550

5 Conclusion Data dimensionality reduction can effectively mine data characteristics and facilitate data visualization. By reducing dimension, redundant information between data can be removed, and the performance of data analysis can be improved. Autoencoder as an efficient deep learning method, is used to achieve the dimensionality reduction for mineral deposit data in this paper. In the process of data dimensionality reduction, Kmeans algorithm is also incorporated to implement intelligent clustering. By measuring the accuracy of clustering and computational time, we can choose the appropriate dimension for those low-dimensional data according to the actual demand. Acknowledgement. This research is funded by the National Key Research and Development Program of China under Grant 2016YFC0600510, the National Natural Science Foundation of China under Grants U1836106 and U1736117, the University of Science and Technology Beijing - National Taipei University of Technology Joint Research Program under Grant TW201705, and the Key Laboratory of Geological Information Technology, Ministry of Natural Resources of the People’s Republic of China under Grant 2017320.

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References 1. Tan Y, Qu H, Wen M (2018) On big data of geological survey. Geomat World 25:7–11 (in Chinese) 2. Lee KC, Ho J, Yang MH, Kriegman D (2003) Video-based face recognition using probabilistic appearance manifolds. In: IEEE computer society conference on computer vision and pattern recognition. IEEE Press, New York, pp 313–320 3. Venna J, Peltonen J, Nybo K, Aidos H, Kaski S (2010) Information retrieval perspective to nonlinear dimensionality reduction for data visualization. J Mach Learn Res 11:451–490 4. Bro R, Smilde AK (2014) Principal component analysis. Anal Methods 6:2812–2831 5. Martinez AM, Kak AC (2001) PCA versus LDA. IEEE Trans Pattern Anal Mach Intell 23:228–233 6. Wang W, Huang Y, Wang Y, Wang L (2014) Generalized autoencoder: a neural network framework for dimensionality reduction. In: IEEE conference on computer vision and pattern recognition workshops. IEEE Press, Columbus, pp 496–503 7. Luo X, Jiang C, Wang W, Xu Y, Wang JH, Zhao W (2019) User behavior prediction in social networks using weighted extreme learning machine with distribution optimization. Future Gener Comput Syst 93:1023–1035 8. Chen M, Li Y, Luo X, Wang W, Wang L, Zhao W (2019) A novel human activity recognition scheme for smart health using multilayer extreme learning machine. IEEE Internet Things J 6:1410–1418 9. Luo X, Sun J, Wang L, Wang W, Zhao W, Wu J, Wang JH, Zhang Z (2018) Short-term wind speed forecasting via stacked extreme learning machine with generalized correntropy. IEEE Trans Ind Inform 14:4963–4971 10. Wang Y, Yao H, Zhao S (2016) Auto-encoder based dimensionality reduction. Neurocomputing 184:232–242 11. Song X, Rui T, Zhang S, Fei J, Wang X (2018) A road segmentation method based on the deep auto-encoder with supervised learning. Comput Electr Eng 68:381–388 12. Liou C, Cheng W, Liou J, Liou D (2014) Autoencoder for words. Neurocomputing 139:84– 96 13. Jain AK (2010) Data clustering: 50 years beyond K-means. Pattern Recogn Lett 31:651–666

The Pressure Control System for Tea Rolling Based on Fuzzy Control Yao Li1, Zhe Tang1,2(&), Fang Qi1, and Chunwang Dong3 1

3

School of Computer Science and Engineering, Central South University, Changsha 410083, China [email protected] 2 ChangSha Xiang Feng Intelligent Equipment Co., Ltd., Changsha 410100, China Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China

Abstract. Rolling is a key procedure in the tea primary processing. The appearance and quality of the final tea produced depends largely on the rolling procedure. The pressure control of tea rolling machine is an issue that often plagues the tea processing enterprises. To solve this problem, a method based on fuzzy control technology has been proposed here. Fuzzy control techniques are used to control the pressure of rolling in this paper. Compared with the traditional PID control method, the pressure control system using fuzzy control has the characteristics of short time to achieve stability and small overshoot. The rolling pressure control system based on fuzzy control proposed in this paper promotes the intelligent control of tea rolling machine. Keywords: Fuzzy control


Tea rolling
Pressure control

1 Introduction With the gradual improvement of people’s living standard, various kinds of tea drinks have gradually entered people’s vision. Tea has become necessary for more and more people because of its rich functions and nutrition. The processing of tea is generally divided into primary production and refining, and rolling is an essential and important part of most of the primary process (except white tea). Rolling can be divided into two main actions, the first is to knead, kneading is to make the tea into strips, the second is to twist, twist can break the cell sap in the tea, let tea juice overflow, so that the overflowing tea juice attached to the surface of the tea strip increases the stickiness between the tea leaves and promotes the formation of the shape of tea. In this way, tea leaves after rolling and drying, can be brewed out of tea flavor and characteristics. Rolling operations, generally more common are manual rolling and mechanical rolling two kinds. At present in addition to some famous tea processing is still a small number of manual rolling, the vast majority of mechanized operations have been achieved. With the change of tea production requirements and the development of PLC control technology, many factories have realized continuous automation of tea rolling operations, but there are still large fluctuations in pressure, pressure size cannot be © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 416–425, 2020. https://doi.org/10.1007/978-981-32-9050-1_48

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adjusted in real time as required, and other deficiencies. Under the condition that the rotation speed and rolling time of tea rolling machine remain unchanged, rolling pressure becomes the main factor to increase the friction force and make the leaves roll into strips. In the process of rolling, different times should adopt different pressure. The pressure will change the friction between tea and tea, tea and the rubbing barrels, and thus directly it affects the degree of tightness and integrity of the tea, so the adjustment of the pressure is one of the key technology of rolling. Naheed et al. [1] studied the effect of the rolling process on the quality of black tea. By setting different rolling time, it was found that the best rolling time of black tea was about 25 min. Bambang et al. [2] applied the fuzzy logic control to the temperature control in the barrel of the tea rolling machine, and achieved the expected goal very well. Park et al. [3] studied the influence of the last stage of tea rolling on the quality of tea, and found that the last stage of tea rolling had a greater impact on the final quality of tea; Ozdemir et al. [4] used three different rolling methods to observe the different physics of tea. It is also one of the factors affecting the quality of rolling that different rolling methods are studied. All of the above researches focus on the influence of rolling time, rolling barrel temperature and rolling method on rolling quality, the related control of rolling pressure have not been addressed systematically. In the early 1970s, after Professor L.A. Zadeh proposed fuzzy control theory, it attracted the attention of many researchers. Fuzzy control is a nonlinear global control technology, which uses the long-term accumulated experience and knowledge of field professionals to formulate relevant rules. The most prominent feature of fuzzy control is that we need not to establish an accurate model. The system only needs perform fuzzy reasoning through the established rule table to complete the control of the system. Fuzzy control can effectively overcome the nonlinearity, time-varying and hysteresis of complex systems and has high control quality. Nowadays, fuzzy control has been greatly improved in both theory and technology, and has been well applied to different control scenarios. Xie et al. [5] applied fuzzy control to the traffic control of urban intersections and found an intelligent traffic control method, which solved the complexity and randomness of urban intersections. Zhang [6] applied fuzzy control to reduce NOx in automobile exhaust, and through the analysis of experimental data, proved that it was feasible to apply fuzzy control to the energy saving and emission reduction of automobile. Zhang et al. [7], in order to control the environmental parameters, applied the fuzzy control technology to the greenhouse and found that it could automatically adjust the parameters on the basis of the needs of plant growth. The fuzzy control technology is applied to tea rolling pressure control. As long as the workers set the pressure according to the actual production needs, the system can adjust the pressing cover position of the rolling machine according to the setting, that is, the rolling pressure can be adjusted. Nowadays, most of the rolling machines have complicated operation and high demand for workers. The pressure control system designed can reduce the dependence of the rolling machines on workers, improve the production efficiency and ensure the quality of tea rolling. Article structure: The second section introduces the fuzzy control, which is divided into four parts: fuzzifier, rule base, fuzzy reasoning and defuzzification, and briefly describes the corresponding input and output variables and rule design. The third section is the experimental part. Matlab/simulink is used for simulation experiment to

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analyze the advantages of fuzzy control algorithm in rolling pressure control, and the results in contrast to the traditional PID method. The last part is about the conclusion and future research direction.

2 Application of Fuzzy Control in Rolling Pressure Control At present, most tea rolling machines in enterprises are barrel rolling machines (the top view of the barrel rolling machine is shown in Fig. 1) [8]. This kind of rolling machines cannot achieve good stable control of the rolling pressure, but the size of the pressure is crucial for the rolling of tea. The pressure is too small to make the cell sap of the tea broken, and the effect of the rolling is not achieved. If the pressure is too high, the leaves will be broken, and the tea leaves will not be beautiful, also cannot meet the requirements of rolling into the rate of enterprise. However, because rolling system has obvious non-linearity and complexity, its accurate mathematical model is difficult to calculate, and fuzzy control technology can just solve these problems, so this paper applies fuzzy control technology to tea rolling pressure control.

Fig. 1. Top view of barrel rolling machine.

2.1

Fuzzy Control Structure

The fuzzy control structure model see Fig. 2. Its structure mainly includes input and output device, fuzzy controller, generalized object and sensor.

Fig. 2. Fuzzy control structure model.

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The fuzzy controller includes: 1. Fuzzifier. The main function is to convert the input variables into the identifiable fuzzy variables according to the actual needs of the system. 2. Fuzzy Rules. Based on the experience of experts or skilled operators, a fuzzy rule base is established. The correctness of the rules directly affects the performance of the controller. 3. Fuzzy reasoning. It mainly implements reasoning decision based on the established rule base, that is to say, according to the input value matching the information of the corresponding rules in the rule base, the control conclusion is drawn. 4. Defuzzifier. The main function is to transform the conclusions from rule base matching into control output. The main methods of de-fuzzification are mom, centroid, bisector, som and lom. 2.2

Structure Design of Fuzzy Controller

A fuzzy controller with double input and single output mode is adopted. Figure 3 is the structure design.

Fig. 3. Two-dimensional Fuzzy Control System for Tea Rolling machine.

The r(t) is the given pressure value, e(t) is an input value of the fuzzy controller, e(t) equals the current pressure minus r(t); The d/dt is the difference between the pressure feedback value at time t and the pressure feedback value at time t-1, that is, the rate of change of the pressure value; Ke and Kc are quantization factors of two input variables of fuzzy controller respectively. The u(t) is the conclusion value obtained by the fuzzy controller according to the set rules, and Ku is the scaling factor of the output variable. The input variables are expressed by E (the difference between the current pressure value and the given value of the rolling machine fed back by the sensor) and EC (the difference between the two connected time points) respectively. The output variables are expressed by U (the value obtained by the fuzzy controller after a series of fuzzification, reasoning and de-fuzzification of the input variables according to the fuzzy rules). 2.3

Membership Function and Rule Design of Fuzzy Control

According to the actual situation of rolling pressure change, input variable E, EC, output variable U were divided into seven fuzzy subsets.

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Because tea leaves are sensitive to rolling pressure, the triangle membership function with simple calculation and implementation, good control performance and high sensitivity is selected. Figure 4 is the membership function curve.

Fig. 4. Curve of variable membership function.

In this paper, based on the long-term experience of experts and skilled professionals, the control rules table is designed in Table 1, where “-” represents the situation that will not occur, for example, E = NB and EC = NB, which means that if the current pressure is far less than the required pressure, and the pressure of the machine will drop rapidly. Obviously, this situation will not occur in the control process. Table 1. Table of fuzzy control rules. u

E

NB

NM

NS

ZO

PS

PM

PB

PB

PM

PM

PS

ZO

ZO

PB

PM

PM

PS

ZO

ZO

NS

-

ZO

PB

PB PB

PM PM

PS ZO

ZO NM

NM NB

NM NB

PS

PM

PM

ZO

NS

NM

NB

PM

ZO

ZO

NS

NM

NM

NB

PB

ZO

ZO

NS

NM

NM

NB

-

EC NB NM

The contents of the above table can use fuzzy conditional statements according to the corresponding fuzzy relation. Mamdani fuzzy control algorithm is adopted for the controller, And method use “min” operation, Or method use “max” operation, Implication set “min” operation, Aggregation set “max” operation, and Defuzzification set “centroid”. The set inference rules can be observed through the browser interface of fuzzy rules, and Fig. 5 is the three-dimensional diagram of the output surface of the fuzzy inference system.

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Fig. 5. Fuzzy reasoning system input and output stereogram.

In Fig. 5, E is the value of the difference between the current pressure value and the given value of the tea rolling machine after quantization factor Ke, EC is the value of the pressure difference between the two connecting time points after quantization factor Kc, and U is the value inferred by the analog controller through two inputs. 2.4

Tea Rolling Pressure Control

This paper applies fuzzy control technology to tea rolling pressure control. There are many different varieties of tea, and different tea varieties have different processing techniques, but the main process is generally the same, divided into water-removing, rolling and drying. In the past, it was manual operation with low efficiency and serious waste of resources. Nowadays most enterprises use tea rolling machine instead of manual rolling, but the pressure in the rolling process is often not well controlled. The method of fuzzy control is adopted to effectively control the rolling pressure, and the pressure sensor is set in the rolling barrel to feedback the rolling pressure value in real time, which is more convenient for workers to adjust the rolling pressure according to their needs. Figure 6 shows the general primary processing process of tea, in which the fuzzy control technology is added to the rolling process.

Fig. 6. Application of fuzzy control in tea primary processing.

3 Experimental Result The computer used in this experiment is windows10-64bit operating system, the software used is matlab_R2016a, and matlab/fuzzy design is used to achieve the fuzzy controller in this paper, matlab/simulink simulation experiment.

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According to the actual situation of tea rolling machine, the transfer function of the control system is G(s) = 10/(3s2 + 2s + 1), the system input is step function, the value is 300, the sampling period is T = 0.4 s, and the simulation time is set as T = 60 s. Because of the force exerted on the tea leaves in the rolling machine, the error range of the initial pressure is [−75, 75], so Ke = 6/75 = 0.08. The pressure value range of the connected sampling time is taken as [−40, 40], Kc = 6/40 = 0.15. Figure 7 is the simulation system structure.

Fig. 7. Structural diagram of simulation system.

In this paper, the fuzzy control algorithm and the traditional PID control algorithm are used to simulate and compare. The simulation results are shown in Fig. 8. The solid line is the result curve of the fuzzy control and the dotted line is the result curve of the PID control.

Fig. 8. Structural diagram of simulation system.

From Fig. 8, it is observed that the time for fuzzy control to reach steady state is about 14 s, and the time for PID control to reach steady state is about 21 s, and there is obvious overshoot in traditional PID control. The final steady-state value of the fuzzy control is 298, which is different from the set 300. The steady-state error is 2, so the system has steady-state error. But 2/300 = 0.67% is acceptable for the pressure control system of tea rolling. Figure 9 is the result by modifying the value of the quantization factor Kc of the fuzzy control.

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Fig. 9. Modify parameter Kc result graph.

In Fig. 9, the response time of the system becomes faster and the time to reach the stable state becomes shorter, but its steady-state value is 296 and the steady-state error is 4. It can be seen that modifying the relevant parameters can change the performance of the system. Since this paper applies fuzzy control to rolling pressure control, considering that tea leaves are sensitive to pressure during rolling, so we choose to apply the system of Fig. 8 to the tea rolling module. Figure 10 is the tea rolling module.

Fig. 10. Application of Fuzzy Control to Tea rolling machine.

Figure 11 shows the tea leaves rolled without pressure control system and with pressure control system. Because the effect of the tea rolled without drying is not obvious, the effect of the tea rolled after drying is compared.

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Fig. 11. Contrast chart of effect after rolling and drying.

In Fig. 11, the left side is the tea leaves that have been rolled by the ordinary rolling machine. In the rolling process, the tea leaves could not get stable pressure control, resulting in poor tea forming effect and incomplete cell breakage, which reduced the quality of the tea leaves that have been rolled. The right side is the tea leaves that have been rolled after applying fuzzy control to the rolling machine. The rolling pressure of tea leaves can be adjusted in real time and stably according to the needs. The tea leaves that have been rolled are well formed and the tea juice can be attached to the surface of tea strips. The rate of tea strips basically achieves the desired goal, and the quality of the twisting is higher than the left side.

4 Conclusion Because the pressure control process of tea rolling machine is non-linear and complex, it is difficult to control its pressure directly and steadily. By applying the fuzzy control technology to the pressure control of tea rolling machine, a pressure control system which can achieve stable state quickly and has small overshoot is designed. The simulation experiment designed in this paper compares the pressure control system under the fuzzy control with that under the traditional PID control. The experimental results prove the superiority of the system under the fuzzy control, which is of great significance to the pressure control of the tea rolling machine. Acknowledgment. This work is supported by the National Key Research and Development Plan (2018YFD0700503, 2018YFD0700500, 2017YFD0400800), Hunan Province Key Research and Development Plan (2018NK2037), Project of Scientific and Technological Breakthrough in Strategic Emerging Industries of Hunan Province (2017GK4006), Science and Technology Project of Changsha City (10200-422040004).

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References 1. Naheed Z, Barech AR, Sajid M (2007) Effect of rolling, fermentation and drying on the quality of black tea. Growth and yield related characteristics of improved commercial 2. Bambang R, Mahbub SF, Abas T (2001) Application of fuzzy logic control to tea rolling process. IFAC Proc 34(11):248–253 3. Park JH, Lim KC (2003) Effect of a final rolling process on Okro tea quality. J Korean Soc Food Sci Nutr 32:58–61 4. Ozdemir F, Gokalp HY, Nas S (1992) Effects of rolling method on physical characteristics of rolled tea leaves. Sri Lanka J Tea Sci 61:51–58 5. Xie ZQ, Liu B (2013) The application of fuzzy control in the intelligent crossroad traffic control. Appl Mech Mater 331:366–369 6. Zhang WJ (2015) The application of fuzzy control in vehicle emission reduction technology. Appl Mech Mater 719–720:306–310 7. Zhang DH, Wu XQ, Zhang CY (2014) The application of fuzzy control in greenhouse environment control. Appl Mech Mater 543–547:1432–1435 8. Haiwei XU et al (2015) Research on the optimal regulation control of barreling pressure for the tea. China Meas Test 41:112–116

Design of Differential GPS System Based on BP Neural Network Error Correction for Precision Agriculture Gangshan Wu1, Chiyuan Chen2, Ning Yang2(&), Haifang Hui2, and Peifeng Xu1 1

Jiangsu Polytechnic College of Agriculture and Forestry, Jurong 211121, China 2 Jiangsu University, Zhenjiang 212013, China [email protected]

Abstract. Precision agriculture is the new tendency of agricultural development all around the world today. During the implementation process, precision agriculture is been required to collect information on crop diseases, pests and fertilizers at any time. The acquisition of this information depends on the precise position information. Considering the problem that the positioning accuracy of the traditional GPS system is low or the other signal needs to be sent to correct the position, a GPS position based on neural network is designed to correct the precision problem. The GPS module was used to receive GPS signal, and the position information was extracted by the MCU. Then, the real-time location information is displayed on the screen. The back propagation neural network was used to generate a prediction model of error value between the measured data and real data. This model can predict and compensate the errors of measured values. Finally, the measured data and the corrected data are shown on the screen. The precision of GPS positioning designed in this paper is 10 times higher than that of traditional GPS, meeting requirement with high precision of the information-based agriculture. Keywords: GPS
Differential Precision agriculture


BP (back propagation) neural network


1 Introduction Precision agriculture applies modern information technology to obtain the difference between natural parameters such as soil and harvesting quantity further to carry out real-time, positioning, and quantitative farming operations. It can not only reduce input, improve crop yield and quality, but also reduce or avoid the loss of chemical fertilizers and pesticides. It is the development direction of China’s agriculture in the future [1]. The acquisition of these data relies on the acquisition of high-precision geographic location information. Therefore, it is necessary to study a simple and practical handheld differential GPS positioning system. As early as the beginning of 1980s, the United States proposed the overall concept of precision agriculture, and then began to enter the practical application phase in the © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 426–438, 2020. https://doi.org/10.1007/978-981-32-9050-1_49

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1990s. In 1992, a team at the University of Pennsylvania in the United States used GPS to conduct a follow-up survey of the migratory agricultural pest, the European corn borer, with the aim of studying its distribution and migration trends, and finally successfully guided the chemical control of the agricultural pest [2]. At present, the practical research of GPS in precision agriculture has been rapidly developed and achieved certain results. The research and application related to precision agriculture have been successively carried out in the United Kingdom, Germany, Japan and other countries [3]. Studies have shown that there is basically no overlap and leakage phenomenon in the control of wheat aphids by GPS, so that the pest control effect is as high as 90% or more, which not only saves the amount of pesticides, but also greatly improves the yield of farmland crops. It has brought a good start to its subsequent development. It can be seen that GPS technology acts a vital role in the implementation of precision agriculture. According to the principle of GPS differential positioning, this paper proposes a GPS positioning method relied on differential positioning technology. The device is simple in structure, low in cost and convenient in operation. In the cloudy weather on March 21, 2019, we used the handheld differential GPS receiver system designed to perform on-the-spot measurements at five landmark locations within Jiangsu University. The measured GPS positioning error was shortened from 20 m to 2 m. The accuracy is increased by 10 times and meets our design requirements.

2 Working Principle 2.1

Basic Principle of Differential GPS

The application of differential GPS (DGPS) technology is based on multiple receivers. First, a receiver is set on the reference station whose coordinate information is known, and the GPS signal is observed in real time by using the receiver of mobile stations. The base station continuously receives the GPS position signal, and then compares it with its known accurate data to obtain various correction values. Then the base station transmits the correction value to the mobile station through radio communication, and finally the observation value of the mobile station is corrected by itself to obtain accurate positioning data, which is the basic principle of differential GPS navigation and positioning technology [4]. 2.2

The Algorithm of Differential GPS

According to the difference of the content of the differential information sent by the base station, the differential positioning method is generally divided into the following two categories: position difference and pseudo-range difference. The working principles of the two types of difference modes are basically the same, and the differences are mainly reflected in the details of the correction values in various modes. So the implementation difficulty and positioning accuracy of the differential positioning

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technology are also different [5]. Below we will introduce the position discrepancy and pseudo-range discrepancy in detail. Position Difference The GPS receiver placed at the base station acquires the positioning information through the observation satellite, and then obtains the coordinates (X′, Y′, Z′) of the reference station at the observation time. However, due to the influence of ephemeris error, clock error and atmospheric refraction during positioning, there is a certain error between the coordinate value and the known coordinate (X, Y, Z) [6], and the correction of its coordinates can be obtained: 8 < DX ¼ X  X 0 ð1Þ DY ¼ Y  Y 0 : DZ ¼ Z  Z 0 In the formula, DX, DY, DZ are the coordinate corrections of the base station. The base station transmits the correction value to the subscriber station by radio, and the user receiver corrects its coordinates by the correction value to obtain its coordinates. 8 0 < Xp ¼ Xp þ DX 0 Yp ¼ Y þ DY ð2Þ : Z ¼ Zp0 þ DZ p p In the formula, X′p, Y′p, Z′p are the results observed by the user receiver, and Xp, Yp, Zp are the corrected coordinate values respectively. If we consider the instantaneous change in the coordinates of the user’s receiver due to the time discrepancy of the data in the transmission process, then the above formula can be written as: 8 dðXp0 þ DXÞ 0 > ðt  t0 Þ > < Xp ¼ Xp þ DX þ 0 dt dðYp þ DYÞ 0 Yp ¼ Yp þ DY þ ðt  t0 Þ dt > > dðZp0 þ DZÞ : 0 Zp ¼ Zp þ DZ þ ðt  t0 Þ dt

ð3Þ

t is current positioning time of user receiver; t0 is the coordinate correction time of the base station. Pseudo-Range Difference The pseudo-range difference means that the distance to the satellite is obtained from the reference station by the known coordinates, and then the obtained distance value is compared with the measured distance containing the error, and the deviation (pseudorange correction number) is obtained, and the value is transmitted to the subscribe station through the data link. The subscriber station uses the value to correct the measured pseudo-range and obtain the position coordinates of the location [7]. The

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corrected value eliminates the common error between the two stations compared with the original value, so the positioning accuracy is effectively improved. The exact coordinates on the base station are (Xi, Yi, Zi), and the geocentric coordinates of each satellite obtained by the observation satellite are (Xj, Yj, Zj), and all pseudo-ranges Ri are measured. Then we can find the true distance from the base station to the satellite at any time: Ri ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðXj  Xi Þ2 þ ðYj  Yi Þ2 þ ðZj  Zi Þ2

ð4Þ

In the formula, the subscript i represents the observed i-th satellite. Thus, the correction of the pseudo-range can be obtained: Dqi ¼ Ri  qi

ð5Þ

Considering the time delay between the differential correction numbers from the reference station (t0) to the user station (t), the change rate of the differential correction number in the propagation process is as follows: 

Dqi ¼

Dqi Dt

ð6Þ



The base station transmits Dqi , Dqi to the subscriber station, The user receiver adds the measured pseudo-range qui to the pseudo-range correction value to find the pseudorange value after the final correction: 

u q0u i ¼ qi þ Dqi þ Dqi ðt  t0 Þ

ð7Þ

3 System Design 3.1

System Hardware Overall Design

Overall GPS hardware design the includes following contents: U-BLOX NEO-6M positioning module, STC15F2K60S2 microcontroller minimum system, TFT32240 RS035BT touch screen and power module. The satellite data received by the GPS module is sent to the serial port of the single chip microcomputer, and then the MCU controls the liquid crystal display to perform real-time display of the positioning information. The system adopts TFT32240RS035BT touch screen liquid crystal display, which is easy to use and has a wide application range. In addition, the voltage module is responsible for supplying +5 V to each chip. The total system hardware design is shown in Fig. 1.

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GPS-OEM

STC15F2K60S2

LCD

POWER

Fig. 1. The total design of hardware.

GPS Module In this paper, a handheld differential GPS positioning system for precision agriculture is designed, so first of all, the GPS positioning module is introduced. Today, There are two main types of GPS-OEM boards: GPS 25 LP [8] and U-BLOX NEO-6M. The serial port of the former OEM board can only send RS-232 level, while the port of the MCU uses CMOS/TTL level. Therefore, in order to communicate with the MCU, the level conversion must be carried out. In order to use conveniently, the system chooses to provide TTL level U-BLOX NEO-6M board. Main Control Module At present, the more commonly used MCUs are Atmel’s AT89C51 [9], Hongjing Technology’s STC89C52RC type and STC15F2K60S2 type. Compared to the AT89C51, the STC15F2K60S2 does not require an external reset and crystal oscillator circuit. In addition, the STC89C52RC has only 8 k of flash program memory and only one set of serial communication ports, while the STC15F2K60S2’s flash program memory has 60 k of storage space and two sets of serial communication ports, which can store the differential table of the design. Moreover, the design of the system requires two sets of serial communication ports. Therefore, the STC15F2K60S2 MCU produced by Hongjing Technology is selected. LCD Module In this paper, the liquid crystal display module selects the TFT32240RS035BT touch screen produced by Guxin Technology, which is a serial port type liquid crystal display specifically for the single-chip control system. The true color TFT screen has the characteristics of high performance and low power consumption. Its interface provides TTL level and can be directly connected to the microcontroller. Therefore, the system selects the TFT32240RS035BT touch screen to design the real-time display of positioning information. 3.2

System Software Overall Design

The overall software design of the system includes the following contents: GPS positioning data extraction, serial port interruption, human-computer interaction

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interface (LCD liquid crystal display), and system initialization. First, the GPS positioning module receives the satellite signal, and then the signal chip computer receives the “$GPRMC” statement through the serial port interrupt mode. When receiving the “$GPRMC” statement, the LCD displays the current positioning information. If the data is received again, the LCD will update the current status. That is to say, the LCD can display the current GPS positioning information in real time. It shows the software design of the entire system in the Fig. 2.

Start

System initialization

N

If “$ GPRMC” Y

Receive and put in the serial buffer Extract UTC time, latitude, longitude and date Query error compensation table Get corrected latitude and longitude

Display original and corrected values for positioning information Fig. 2. The overall design of system software.

GPS Module Receiving Program Design The GPS module receiving program design is mainly responsible for receiving the serial data sent by the U-BLOX NEO-6M board. This program is completed in the serial port interrupt. This GPS-OEM board can output a variety of different format statements. The design only needs to receive data with the “$ GPRMC” as the leader, and the format of the statement is: $ GPRMC, , A, , N, , E,, ,, ,,, A*

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represents UTC time. represents latitude value. represents longitude value. represents ground speed, and the unit is section. represents UTC date, and represents checksum. The design only needs to extract the latitude and longitude, date, and world standard time in the data frame. The design adopts the method of interrupt receiving to receive GPS data, and when the serial port receives the data, it is stored in the array of buff[]. And a serial port interrupt is generated when one frame of data is received. At this point, the CPU performs data extraction in response to the interrupt request. LCD Display Program Design This system selects TFT32240RS035BT liquid crystal display for real-time display of positioning information. The design generally needs to go through the following steps: serial port initialization, liquid crystal clear screen, palette setting and interface display. Construction Method of Difference Operation The paper is designed to be a differential GPS for information-based agriculture, but the existing GPS receiver is a single point positioning, whose positioning accuracy cannot meet the high precision requirements of precision agriculture. Therefore, it is necessary to construct a differential algorithm to improve the accuracy of the positioning by performing error compensation correction on the original measured values. Because the relationship between the measured values and the errors is unknown, BP neural network is utilized to construct model. The method considers unknown system as a black box. Firstly, a known BP neural network is constructed by using the known measured values and errors as the input and output of the system. The network can simulate a certain correspondence between the known measured values and errors by repeatedly training the known data, and then obtain the BP neural network prediction model. A variety of different neural networks have emerged, of which BP neural networks are most commonly used. As can be seen from Fig. 3 below, there are three layers in the network, each layer needs to be interconnected, while the units in the same layer are independent and do not have association.

Input

Input

Hidden layer

Output

Output

Fig. 3. Typical 3-layer BP neural network architecture.

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4 Results and Discussion 4.1

Experimental Materials

The design of the system consists of the following four modules: U-BLOX NEO-6M positioning module, STC15F2K60S2 microcontroller minimum system, TFT32240 RS035BT touch screen and power module. 4.2

Experimental Steps Step 1: Debugging of the GPS-OEM board. Connect the UART GPS NEO-6 M module, the serial port and the computer. After the setup is completed, the OEM board is placed outdoors. After a few minutes, you can see the data output (GPS receiving statement) in the serial debugging assistant, indicating that the OEM board is successfully positioned. Step 2: Module combination. The GPS module, the MCU minimum system, the LCD screen and the power module are all fixed on a fiberglass board. Connect the TXD and RXD pins of the GPS module to P1.0 and P1.1 of the MCU respectively. Connect the TXD and RXD pins of the LCD display to P3.0 and P3.1 of the MCU respectively. Finally, connect the VCC and GND pins of each module to the power module to complete the hardware part. Step 3: Download the completed program of the entire system to the signal chip computer to complete the work of the software part. Step 4: Take the circuit board to an open space, and then turn on the power. After powering on the MCU, observe whether the LCD screen works normally. If the positioning information can be displayed normally, it means that the system is successfully debugged.

4.3

BP Neural Network Modeling

The system selected 25 sets of known record to train the neural network. The data were obtained by measuring in different places in Jiangsu University. Then input 5 sets of data to be predicted (original latitude and longitude measurement values), the trained neural network can predict the corresponding output (error). Finally, the original value is corrected by using the error compensation value, and the corrected positioning data can be obtained. As shown in Fig. 4, the blue dots represent the input known longitude values (measured values) and their corresponding error data, which are obtained by subtracting the measured longitude values from the exact values found on Google Earth. The neural network is trained by these 25 sets of data, and the red dots represent the predicted neural network to predict the error output value corresponding to the original data, so that the prediction model of the longitude value is successfully established.

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Fig. 4. Prediction model of the longitude and error value.

Under the prediction model, the five new input longitude values are predicted, and the error compensation value of the longitude is included in the following Table 1. Table 1. Error compensation table of the longitude. Location

Central gate Sanshan building E119.51352 E119.51030

Longitude measurement Error compensation 1.9  10−4 1.7  10−4 value

Newtown 1 Library

Electronic building E119.51073 E119.50599 E119.50421 2.1  10−4 0.9  10−4 1.5  10−4

Similarly, neural network training is also performed on latitude values. The training results are shown in Fig. 5 below. The blue dots represent the input known latitude values and their corresponding error data. The neural network is trained by these 25 sets of data. The red dot represents the trained neural network to predict the error output value corresponding to the original data. A predictive model of latitude values is successfully established.

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Fig. 5. Prediction model of the latitude and error value.

Under the prediction model, the five new input latitude values are predicted, and the error compensation value of the latitude is counted in Table 2. Table 2. Error compensation table of the latitude. Location Latitude measurement Error compensation value

4.4

Central gate Sanshan building N32.19599 N32.19715

Newtown 1

Library

N32.20537

N32.20032

Electronic building N32.19954

−0.9  10−4 −1.3  10−4 −1.2  10−4 −1.6  10−4 −1.1  10−4

Experimental Results and Analysis

The hardware chart of the GPS is shown in the Fig. 6. The system is mainly composed of U-BLOX NEO-6M positioning module, STC15F2K60S2 MCU minimum system, TFT32240RS035BT touch screen and power module.

Fig. 6. Hardware physical map.

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Connected to the power supply, when the GPS-OEM board is placed indoors or where the signal is weak, the GPS cannot be accurately positioned. At this time, the LCD displays “Date error” (as shown in Fig. 7 below), indicating that the current positioning has failed.

Fig. 7. Invalid location state of GPS.

When moving to the outdoor, the LCD displays “Data success” and displays the current positioning data after the GPS is successfully positioned. As shown in Fig. 8 below, the longitude, latitude, date, and time are from top to bottom. The latitude and longitude on the left is the original measured value, and the right side is the corrected value obtained after error compensation.

Fig. 8. Successful positioning state of GPS.

Five locations were selected for field measurements in Jiangsu University. There are Central gate, Sanshan building, Newtown1, library and electronic building respectively. The original latitude and longitude values measured at the five locations and the corrected results obtained by the error compensation are now counted in Table 3 below.

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Table 3. Experimental results. Newtown1 E119.51096, N32.20537

Library E119.50611, N32.20013

E119.51352, N32.19599

Sanshan building E119.51050, N32.19697 E119.51030, N32.19715

E119.51073, N32.20552

E119.50599, N32.20032

Electronic building E119.50437, N32.19939 E119.50421, N32.19954

E119.51371, N32.19590

E119.51047, N32.19702

E119.51094, N32.20540

E119.50608, N32.20016

E119.50436, N32.19943

18.8

23.9

25.5

23.1

17.3

1.9

3.1

2.3

2.2

1.7

Location

Central gate E119.51373, N32.19587

The original value (unit: degree) The corrected value (unit: degree) The original error The corrected error

As can be seen from Table 3, the measured error of GPS single point positioning is about 20 m, and the accuracy after error compensation can reach about 2 m, which can basically meet the requirements of information-based agriculture for positioning.

5 Conclusion The paper aims to design a handheld differential GPS positioning system for precise agriculture, which involves the combination of software and hardware. First, the UBLOX NEO-6M positioning module is used to receive GPS data information. Then positioning related information is extracted by the serial port interrupt mode of STC15F2K60S2 microcontroller via GPS system, and subsequently sent via the serial port for the liquid crystal display of TFT32240RS035BT. Finally the LCD is used to display the positioning information. The software construct of the system mainly involves the function of differential operation. The BP neural network was unitized to generate the prediction model. This model can predict and compensate the errors of measured values. After debugging by software and hardware, a real-time differential GPS system is managed in this paper. The original accuracy of GPS single-point position is about 20 m, and the positioning accuracy corrected by error compensation method is about 2 m, indicating greater accuracy than the original data, which greatly improves the positioning accuracy of the GPS receiver. Thus, this technique can basically meet the precision of the information-based agriculture positioning. Acknowledgements. This research was financially supported by the Research on Intelligent System for Early Diagnosis of Main Strawberry Pests and Diseases Based on Deep Learning (2018kj11), Chinese National Natural Science Foundation (31701324), China Postdoctoral Science Foundation Project (2018M642182), Jiangsu Agricultural Science and Technology

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Innovation Fund (CX(18)3043), Outstanding Youth Science Foundation of Jiangsu province (BK20180099), Zhenjiang Dantu Science and Technology Innovation Fund (Key R&D ProgramSocial Development) (SH2018003), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Project of JIANGSU POLYTECHNIC COLLEGE AGRICULTURE AND FORESTRY (2018kj12).

References 1. Yost MA (2019) Public–private collaboration toward research, education and innovation opportunities in precision agriculture. Precis Agric 20(1):4–18 2. Shockley JM (2019) An economic feasibility assessment of autonomous field machinery in grain crop production. Precis Agric (2):1–18 3. Yong L (2012) Attitude determination by integration of MEMS inertial sensors and GPS for autonomous agriculture applications. GPS Solutions 16(1):41–52 4. Rudolph S (2018) Assessment of the position accuracy of a single-frequency GPS receiver designed for electromagnetic induction surveys. Precis Agric (3):1–21 5. Clark TE (2005) Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis. J Econom 135(1):155–186 6. Tan H (2006) DGPS-based vehicle-to-vehicle cooperative collision warning: engineering feasibility viewpoints. IEEE Trans Intell Transp Syst 7(4):415–428 7. Rezaei S (2007) Kalman Filter-based integration of DGPS and vehicle sensors for localization. IEEE Trans Control Syst Technol 15(6):1080–1088 8. Harwin S (2012) Assessing the accuracy of georeferenced point clouds produced via multiview stereopsis from unmanned aerial vehicle (UAV) imagery. Remote Sens 4(6):1573–1599 9. Qiu J (2013) Mitochondrial calcium uniporter Mcu controls excitotoxicity and is transcriptionally repressed by neuroprotective nuclear calcium signals. Nat Commun 4(3):2034

Multi-view Based Pose Alignment Method for Person Re-identification Yulei Zhang, Qingjie Zhao(&), and You Li Beijing Key Lab of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing 100081, China {zyl1260,zhaoqj}@bit.edu.cn, [email protected]

Abstract. This paper proposes a Multi-View based Pose Alignment (MVPA) method for person re-identification (re-id). Most recent methods solve re-id as a matching process based on single image. However, when poses vary or viewpoints change, the performance seriously deteriorates. This paper aims to learn a representation insensitive to view and pose. Specifically, we establish a set of Multi-view based Person Pose Templates (MPPT) and propose a Pose-Guided Person image Generation (iPG2) model to synthesize multi-view and uniformpose based images. The representation learned from multi-view images can significantly enhances the accuracy of re-id. We evaluate our method on two popular datasets, i.e., Market-1501 and DukeMTMC-reID. The results show that our framework promotes the performance of re-id a lot and surpass other methods. Keywords: Person re-identification Generative Adversarial Networks


Pose Alignment


1 Introduction The purpose of person re-identification is to recognize a particular person who has been observed elsewhere [10]. It has raised extensive discussions due to widespread applications, e.g., video surveillance and criminal tracking. Though many methods aim at solving problems of low-resolution, occlusion, lighting, etc, the critical challenges are still remained such as pose variations and viewpoint changes. To solve this problem, some approaches utilize external pose information and design models to normalize poses of images. For example, [14] uses a PoseBox structure to align people to a standard pose, and designs a Pose-Box fusion (PBF) CNN architecture to execute the person re-id task. These approaches use single-pose based images to learn representations, which can partially solve the problem of pose variations. However, how to deal with images with many viewpoints changes is still an unsolved challenge. In order to solve the problems above, we propose a Multi-View based Pose Alignment (MVPA) method to learn a pose-and-view-insensitive representation. The contributions of this paper can be summarized as follows:

© Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 439–447, 2020. https://doi.org/10.1007/978-981-32-9050-1_50

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(1) We establish a set of Multi-view based Person Pose Templates (MPPT), which contains eight pose templates. Each template is a binary image corresponds to a typical pose of person in a specific viewpoint. (2) We propose an improved Pose-Guided Person image Generation (iPG2) model to generate uniform-pose based images. The traditional image generation model is modified by transforming the training mode and loss functions. (3) We design a Multi-view based Pose Alignment(MVPA) method for re-id, which can properly face the problem of pose variation and viewpoint change.

2 Related Works In this section, some image generation models and deep re-id models are discussed, since our framework is concentrated on pose-guided image generation and deep re-id models. 2.1

Image Generation Models

Generative Adversarial Networks (GAN) [4] can generate new images with novel features excluded in datasets, which is widely used recently. It learns high dimensional and complex distribution of data in original images and transports it to the new images. As a result, generated images have the same inherent features as original images. Recently, GAN has been applied in domain of reid. Many methods use it for data augmentation. For example, [16] uses GAN to generate new images. However, the generated images have no relation with the original images, so they are classified as unlabeled images, which increases the diversity of pose variations, instead. Consequently, the results of re-id don’t have much progress. Aiming to solve this problem, [8] proposes SimPGAN to preserve the similarity of the transformed images of the same person. However, the transformed images still have different poses, which makes feature learning model sensitive to pose variations. It is used for cross-dataset re-id, thus we don’t compare our method with it. Differently, [9] proposes a two-path Pose-Guided Person image Generation (PG2) model to generate uniform-pose based images. It can generate an image of the same person with the target pose who is in the source image. This approach creatively solves the problem of pose variation. But in re-id process, a singlepose based representation may lead to mismatching due to viewpoint changes. In experiments, it serves as baseline method for our approach. We will show its weekness and the advantages of our method in Sect. 4. Our approach solves both the problems of pose variation and viewpoint change together by generating multi-view and uniform-pose based images without identity change. Besides, we improve the PG2 model by training the two generators simultaneously and changing its loss functions. 2.2

Re-id via CNN Models

Deep Convolutional Neural Networks (CNN) is popular in task of image classification and feature learning. They concentrate on designing lots of complex architecture and

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loss functions, such as identity classification, pairwise loss and triplet loss. However, they hardly pre-process images with problems of pose variation and viewpoint change. In our method, we first align the person’s pose with iPG2, and then we use a CNN architecture to learn a more robust representation with generated images and the source image.

3 Methods Problem Definition and Method Formulation. Assume we have two gallery sets: a query set of N persons Dquery ¼ fIk ; yk gNk¼1 and a testing set of M persons Dtest ¼ fIk ; yk gM k¼1 , where Ik and yk are the person images and the number of the k-th person, respectively. Given an image of query set fIi ; yi g and an image of testing set fIi ; yi g, we need to judge whether yi ¼ yj or yi 6¼ yj . The formulation of our method is shown in Fig. 1. We firstly establish a set of Multi-view based Person Pose Templates (MPPT). Then, for every source image, we use the improved image generation model to synthesize eight uniform-pose based images. Finally, we obtain a pose-and-viewinsensitive representation by fusing features learned form the eight generated images and the source image.

Improved PG2

Deep Re-id Model

Pose Templates

Result 1

Result 8

Source Image

Feature 1

Feature 9

Re-id Representation

Fig. 1. Overview of our MVPA framework.

3.1

Multi-view Based Person Pose Templates

This template set contains 8 pose templates of different viewpoints from 0° to 360° increasing by 45° (see Fig. 3(b)). Concretely, we first take a video of a person walking toward all directions and capture a image with typical pose for every viewpoint. Secondly, for each image, as Fig. 3(a) shows, we use a off-the-shelf pose detection toolkit – OpenPose [1] to extract skeleton of the person. Then, we obtain a binary image with 18 anatomical key-points and we connect these keypoints with a line of three-pixel-width. Finally, we get a pose template by taking morphological operations

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on the pose skeleton image. Specifically, the person of this template set is beyond datasets, otherwise the image generation model tends to directly generate images of the same person (Fig. 2).

OpenPose Target Image

Key Points

Original Template Images

Connect

morphological

operation Pose Image

Pose Skeleton

(a)

Multi-view based Person Pose Templates (b)

Fig. 2. Production the Multi-view based Person Pose Templates. (a) shows the process of a single template production. (b) shows the original template images and the NPPT.

3.2

Improved Pose-guided Person Image Generation Model

We propose an improved Pose-Guided Person image Generation (iPG2) model to synthesize identity-invariant and uniform-pose images. Specifically, in training time the model randomly sample two images of a person with different poses, one of which is served as source image Ii and another is served as target image Ij. We train the model to output an image with pose of the target image without identity change. While in testing time, the model takes a image of templates as target and transforms the person of source image into the target pose. As Fig. 3 shows, our model has a two-path generator and a discriminator. The first generator (G1) focus on generating a course result ^Ij which generally has the target pose and global feature of the source image. To enhance 0 local details, generator (G2) compares the difference of local feature between Ii and ^Ij which outputs a difference map. Subsequently, we obtain a more clear and real-like 0 result ^Ij by refining course result via difference map. As for discriminator, it distinguishes images from generator or datasets by sampling image pairs rather than single real or fake image. This operation can effectively avoid misleading G2 to directly output source image. Now, we introduce the architecture and loss functions of our model.

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Skip Connection

Source Image

Target Image

Coarse

Pose Template

Result

Generator 1 (G1)

Source Image

Skip Connection

Real

or Fake

Generator 2 (G2)

Difference

Final

Map

Result

Source Image

Discriminator (D)

Fig. 3. The architecture of iPG2. G1 generates a course image with the desired pose. G2 compare the course result with source image, which outputs a difference map. D distinguish real and fake images.

Generator G1. G1 is a U-Net-like architecture. Both the encoder and decoder has five residual blocks and they are symmetric except for the last layer of decoder which generates an image of three channels. There is a fully connected layer between them. The residual block consists of two convolutional layers with stride of one and two, respectively. To avoid information lost because of deep networks, skip connections are used between symmetric layers in encoder and decoder. Generator G2. The architecture of G2 is similar to that of G1. G2 is also a U-Net-like architecture. Different to G1, G2 aims to learn shallow features, so both of the encoder and decoder have only three residual blocks and the fully connected layer between them is removed. Besides, skip connections are also adopted in G2. Discriminator D. Discriminator is a fully convolutional network. It has four residual blocks, which has the same architecture as that of G1, except for the last block, the second layer of which is replaced by a sigmoid unit. Improvements. In this paper, we improved the traditional pose guided image generation [9] by training the G1 and G2 simultaneously, which are separately trained in traditional method. Thus, the loss function will be different and the training time will decrease. Now, we analyze the loss functions as follows: Loss Functions. We first give the loss function of discriminator. The discriminator should distinguish fake pairs and real pairs. Thus, the loss function of D can be formulated as Eq. 1, LD ¼ Lbce ðDðIi ; Ij Þ; 1Þ þ Lbce ðDðIi ; GðIi ; ^Ij ÞÞ; 0Þ;

ð1Þ

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where Lbce denotes binary cross-entropy loss, D denotes the discriminator, G is output of G2. Similarity, the function of real-like loss can be described by Eq. 2, Lreallike ¼ Lbce ðDðIi ; GðIi ; ^Ij ÞÞ; 1Þ;

ð2Þ

where G is the output of G2. For generators, besides real-like loss, we should take absolute loss into consideration. And to make generators focus more on region of body rather than background, we set the proportion of their loss as 2:1. Thus, the loss function of G1 can be described as,

LG1 ¼ Lreallike þ k ð^Ij  Ij Þ  ð1 þ Mj Þ 1 ;

ð3Þ

Where the symbol  means pixels-wise multiplication. Mj is a pose mask with 1 for body region and 0 for background region, k denotes the proportion between real-like loss and absolute loss. Be similar to G1, the formulation of G2 loss can be described in Eq. 4,

LG2 ¼ Lreallike þ k ðGðIi ; ^Ij Þ  Ii Þ  ð1 þ Mj Þ 1 ;

3.3

ð4Þ

Re-id with Deep Learning Model

We use ResNet-50 [11] as our person re-id feature learning model. As Fig. 1 shows, we obtain a view-and-pose-insensitive representation via fusing features learned from 8 multi-view and uniform-pose based images and the source image. Specifically, we obtain representations both for the query image and testing image. Finally, the similarity of them can be calculated by Euclidean distance.

4 Experiments We experiment our method on two popular datasets: Market-1501 [15], DukeMTMCreID [12]. We show qualitative and quantitative results of these datasets, and compare our framework with recent methods. 4.1

Qualitative Results

Figure 4 shows samples of generated images of our method on the two datasets. Specifically, row 1, 2 are representative samples of Market-1501, DukeMTMC- reID, respectively. While column 1 shows the source image and column 2–9 are generated images. We can conclude from the qualitative results that our MVPA method can effectively face the problem of pose variation by synthesizing identity- invariant images with target poses.

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Generated Images

Market1501

DukeMT MC-reID

Fig. 4. Samples of generated images of our proposed MVPA method.

4.2

Quantitative Results

We further calculate quantitative results of MVPA using two evaluation metrics, i.e, Rank accuracy, mAP (mean Average Precision), which are widely used in person re-id domain. We show Single-query and Multi-query results for Market- 1501, only Singlequery for DukeMTMC-reID. We simply compare results on supervised learning.

Table 1. Results of re-id. “SQ”: Single-query. “MQ”: Multi-query. “-”: No reported result available. Red numbers show the best result overall. Bold numbers show our method’s results.

Datasets Market-1501(SQ) Market-1501(MQ) DukeMTMC-reID Metric(%) Rank-1 mAP Rank-1 mAP Rank-1 mAP IDE+ML [5] 81.6 71.4 86.8 79.8 OGSL [6] 87.1 70.2 76.4 63.7 EFE [3] 87.7 71.1 93.3 79.3 75.7 58.2 DPFL [2] 88.6 72.6 92.2 80.4 79.2 60.6 LSRO [16] 78.1 56.2 85.1 68.5 DSA [13] 84.3 64.7 70.7 51.9 HA-CNN [7] 91.2 75.7 93.8 82.8 80.5 63.8 Basel. 73.1 60.3 79.7 65.9 65.6 47.7 MVPA(Ours) 90.3 75.5 94.6 83.5 81.2 67.9

Comparison Baseline with MVPA. We show results of baseline and MVPA method as last two rows in Table 1. The baseline method is used as single-view based generation image for representation. Then MPPT is not used. While our MVPA method using multi-view based generation image, which beat baseline by 17.2% at rank-1 and 15.2% at mAP in single-query of Market-1501. It can be concluded that our method can properly face the problem of viewpoint change and pose variation.

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Comparison MVPA with Recent Methods. The first two rows of Table 1 are methods based on triplet loss. The two rows in the middle are results of discriminative feature learning method based methods. While the subsequent three methods aim at problem of pose variation. We observe clearly that methods solving problem of pose variation have a slight edge over other methods, on average. It implies that our proposed method has solved the critical problem of person re-id. Moreover, MVPA outperforms a little inferiorly than HA-CNN [7] in single-query of Market-1501, while in other results we exceed HA-CNN and outperforms the best overall, instead. Compared to Market-1501, DukeMTMC- reID has more viewpoint changes, both in background and person, since images are sampled from 8 synchronous cameras, which is more practical in real world. We can conclude that our method has a more powerful ability to handle the problem of pose variation and viewpoint change.

5 Conclusion We propose a multi-view based pose alignment method for person re-id. We address both the problems of pose variation and viewpoint change by synthesize uniform-pose based images in 8 different viewpoints. To speed up the training time, we change the training mode and update the loss functions of the traditional image generation model. Compared to other person re-id methods which use single-pose based image for matching, our method not only handle the problem of pose variation, but also solve the challenge of viewpoint change by fusing multi-view and uniform-pose based images with the source image. Experiments on two popular datasets of re-id show that our method can properly face the problem of pose variation and viewpoint change.

References 1. Cao Z, Simon T, Wei S-E, Sheikh Y (2017) Realtime multi-person 2D pose estimation using part affinity fields. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 7291–7299 2. Chen Y, Zhu X, Gong S (2017) Person re-identification by deep learning multi-scale representations. In: Proceedings of the IEEE international conference on computer vision, pp 2590–2600 3. Gong A, Qiu Q, Sapiro G (2018) Virtual cnn branching: efficient feature ensemble for person re-identification. arXiv preprint arXiv:1803.05872 4. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial networks. In: Proceedings of advances in neural information processing systems (NIPS), vol 3, pp 2672–2680 5. Hermans A, Beyer L, Leibe B (2017) In defense of the triplet loss for person reidentification. arXiv preprint arXiv:1703.07737 6. Jiang N, Liu J, Sun C, Wang Y, Zhou Z, Wu W (2018) Orientation-guided similarity learning for person re-identification. In: 2018 24th international conference on pattern recognition (ICPR). IEEE, pp 2056–2061

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7. Li W, Zhu X, Gong S (2018) Harmonious attention network for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 2285–2294 8. Lv J, Wang X (2018) Cross-dataset person re-identification using similarity pre-served generative adversarial networks. In: Proceedings of international conference on knowledge science, engineering and management (KSEM). Springer, pp 171–183 9. Ma L, Jia X, Sun Q, Schiele B, Tuytelaars T, Van Gool L (2017) Pose guided person image generation. In: Proceedings of advances in neural information processing systems (NIPS), pp 406–416 10. Plantinga A (1961) Things and persons. Rev Metaphys 14:493–519 11. Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, pp 1–16 12. Ristani E, Solera F, Zou R, Cucchiara R, Tomasi C (2016) Performance measures and a data set for multi-target, multi-camera tracking. In: Proceedings of European conference on computer vision (ECCV), pp 17–35 13. Zhang Z, Lan C, Zeng W, Chen Z (2018) Densely semantically aligned person reidentification. arXiv preprint arXiv:1812.08967 14. Zheng L, Huang Y, Lu H, Yang Y (2017) Pose invariant embedding for deep person reidentification. arXiv preprint arXiv:1701.07732, pp 1–10 15. Zheng L, Shen L, Tian L, Wang S, Wang J, Tian Q (2015) Scalable person re-identification: a benchmark. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 1116–1124 16. Zheng Z, Zheng L, Yang Y (2017) Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 3754–3762

A Novel Contribution Graph Based Likert Scale Method and Its Application to Real-Time Alarm Evaluation Qun-Xiong Zhu1,2, Rui Ding1,2, Yan-Lin He1,2(&), and Yuan Xu1,2(&) 1 College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China {heyl,xuyuan}@mail.buct.edu.cn 2 Engineering Research Centre of Intelligent PSE, Ministry of Education of China, Beijing 100029, China

Abstract. In the complex process industry, alarm management plays a very important role in industrial safety production. In a traditional alarm system, alarm flooding occurs frequently due to a large number of single-variable threshold alarms, which seriously affects the operator’s judgment on the order of alarm processing. For timely processing of alarms, the division of alarm priority is very important. In order to solve this problem, a novel method using Principal Component Analysis (PCA) contribution graph based Likert scale is proposed for online division of the priority of alarm variables. Different from the traditional Likert scale method, the method proposed in this paper can analyze the influence degree of each variable on the stability of the system and prioritize the alarm variables in real time. In addition, key alarm information among redundant alarms can be exactly extracted. The effectiveness of the method is verified by the Tennessee Eastman Process (TEP). Keywords: Industrial safety
Alarm management Likert scale
Contribution plot analysis


Alarm priority


1 Introduction In complex process industries, the alarm system is an important protective layer for production safety. The performance of alarm systems directly affects production safety, product quality and product expense. According to the Abnormal Situation Management (ASM) statistical results, the process industry loses billions of dollars due to accidental parking every year. At the same time, the emergence of a major accident will be accompanied by a large number of false alarms. The key reason for the above problems is that the number of alarms is too large, far exceeding the ability of operators to handle alarms. This problem is called as “alarm flooding”. There are some complex features in modern complex process industries, such as large-scale uncertainty, strong correlation, high nonlinearity, and so on. When a malfunction occurs in one part of the system, the fault will propagate along the path of the process, causing a great number of alarms in other parts in an instant, causing ‘alarm flooding’. This situation can cause the © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 448–455, 2020. https://doi.org/10.1007/978-981-32-9050-1_51

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operator’s attention to be distracted. To solve the above problems, alarm variables can be ranked by prioritization, and the key variables are processed centrally. Currently, according to The Engineering Equipment and Materials Users’ Association (EEMUA) standard, there are two criteria for prioritization criteria: the severity of the incident and the time where the alarm is processed. Ahmed and Chang [1] proposes an imploded model that includes event occurrence probability (P), potential event impact (I), and process safety time (t) to order alarms. Although the method considers the prioritization criteria, the assessment of the severity of the consequences and the time available for the alarm is very subjective. Zhang [2] based on the key alarm grouping and suppression strategy of process data made a detailed scoring rule on the severity of the consequences to achieve alarm priority division; Zhao et al. [3] divided the priorities based on the combination of response time and consequence level matrix. The above methods divided the alarm variables by adding the impact factor to combine the severity of the aftermath with available time, but the method of division is relatively subjective. Zhu and Geng [4] uses the fuzzy clustering algorithm and deference driving decision algorithm sort alarm variables. Foong et al. [5] proposed to develop and apply Mamdani’s system inference engine to divide alarms priority. This method has been widely used to System and problem analysis in numerous fields. Gao et al. [6] quantified the alarm variables based on the interpretation structure model and the Likert scale method; however, only the number of alarms as a scoring criterion cannot reasonably indicate the degree of influence of each variable on the whole system, and the priority order cannot be adjusted as faults change. The traditional prioritization calculation method model is inaccurate and cannot be prioritized in real time. In order to resolve this problem, a novel method using Principal Component Analysis (PCA) contribution graph based Likert scale is proposed for online division of the priority of alarm variables. The method proposed in this paper can analyze the influence degree of each variable on the stability of the system, and analyze alarm variables in real time. The remaining sections of this paper are organized as follows: Sect. 2 introduces the traditional Likert scale analysis method and PCA contribution graph; Sect. 3 introduces the real-time alarm variable evaluation method based on the contribution graph and the Likert scale proposed in this paper; Sect. 4 carries out the Tennessee Eastman Process (TEP) simulation as a research case to validate the effectiveness and practicability of the proposed method; Sect. 5 summarizes the full text.

2 Preliminary Methods 2.1

Based on the Traditional Likert Scale Prioritization

Likert scale is also called Score plus total scale. American social psychologist Likert first proposed this method in 1932. The Likert scale Use Relativ Importance Index (RII) to help with statistics analysis. Since the Likert table method can quantify the subjective experience, the alarm priority can be combined with the Likert scale and RII. The traditional Revised Likert Scale (RLS) uses true historical data to determine the score of various properties, and the corresponding index is used to prioritize the alarm

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variables. Before the RLS is established, two points need to be explained: First, the scoring criteria are based on the Alarm frequency per unit time; Second, the number of alarms at each level is pre-divided according to the distribution ratio of EEMUA. That is, low, intermediate, and advanced alarm variables account for 80%, 15%, and 5% of the total variables, respectively [6]. 2.2

Traditional PCA Contribution Graph

Traditional PCA is a data-driven dimension reduction method. In data processing, the data variances are the main direction, and the small variance is the noise direction. By maximizing the variances, the data’s original information is preserved while reducing he dimensions of the data. Assuming the original data X 2 Rij , where j is the number of variables, and i is the number of sample V 2 Rj , the method of calculating the projection matrix of PCA is as shown in Eq. 1: max

vT X T Xv vT v

ð1Þ

Replace the X with the covariance matrix S of the sample according to the Eq. 2, where X obeys the Gaussian distribution: 1 pffiffiffiffiffiffiffiffiffiffiffi X ¼ UV T n1

ð2Þ

The singular value division of the data by Eq. 3 can obtains the variance in all directions of the data, and retains the information of each direction of the original data set X: S¼

1 X T X ¼ VKV T n1

ð3Þ

The information in each direction accounts for the value of the main diagonal in K, arrange the values on the main diagonal of K in descending order. Then obtained information of the Kth principal is greater than the ðK þ 1Þth principal. Normally, the PCA method selects a variable that retains 90% information of the original data as a reconstructed principal component by Eq. 4: T ¼ XPa

ð4Þ

Where Pa is the loading matrix Pa 2 Pam , and a is the number of principle variable, n-a is the number of residual variables According to the PCA theory, in the case of a fault occurred in the process system, the T2 statistic calculated by the score matrix T generates an outlier. PCA can only be used for fault detection and cannot be effectively analyzed for the root cause of the fault. The 2D contribution graph [7] can analyze the influence of each dimension variable in X on the scoring matrix T on the basis of PCA. It is presumed that the

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scoring matrix T and the observed value x are obtained. At each moment, the variable is for the total score T. The contribution value is calculated by Eq. 5: (t P X ; if dt2 Pm;n Xn [ 0 d2n n;m n n conðm; nÞ ¼ 0; if t P X \0 d2n

m;n

ð5Þ

n

Where Tm is the mth principal element variable, d2m is the variance of Tm , Pn;m is the ðm; nÞth element of the projection matrix. Throughout the process, the contribution of the raw data Xm to the overall process is shown in Eq. 6: CONT ðnÞ ¼

Xa i¼1

conðm; nÞ

ð6Þ

Different from the traditional 1D contribution graph analysis, the 2D- contribution graph analysis can not only determine the degree of influence of each variable in the overall fault, but also intuitively observe which variables are affected by the stability of the system at different moments. The study of 2D contribution graphs has significant applications for the development of fault diagnosis.

3 The Proposed Method Based on the traditional Likert scale analysis, only the number of alarms per unit time in the historical data is used as the scoring criterion of the Likert scale, and it is impossible to deeply evaluate the actual importance of each variable of the entire system unit. And through the prioritization of historical data, only a fixed priority

Process data(s(t))

PCA-based 2D contribution graph N Alarm generation Y Revised Likert Scale for Alarm Prioritization Output:Final Alarm priority

Fig. 1. Online updates flow chart.

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variable can be obtained. When the root source of the alarm is changed, the importance and priority of each variable are bound to change, so the variable cannot be used only by the historical data and the number of alarms. Dynamic adjustments result in inaccurate analysis of alarm information, which is not conducive to industrial safety. As shown in Fig. 1, the method proposed in this section will be based on the combination of Likert scale and PCA contribution, real-time prioritization of alarm variables. 3.1

Filter Based on Contribution

The traditional PCA-based 2D contribution graph only considers the influence of each variable on the whole system, and ignores the influence of multivariate on the system, making it impossible to distinguish key root variables. This paper adds 3 sigma filtering to the 2D contribution graph to enhance the system’s analysis of key variables. The corresponding score matrix Tnew and the contribution degree conz can be obtained by calculating the original data set X by the Eqs. 4 and 5. Due to the affect of the extent of change of each variable on the system, in order to make each variable represent its own proportion of change, perform the screening as in Eq. 7:  conzðm; nÞ ¼

1; if conzðm; nÞ  3d2 0; if conzðm; nÞ \ 3d2

ð7Þ

where d2m represents the variance of the sequence of contribution rate of each variable under the nth sampling point. By formula 7, it is possible to screen out variables that have a greater impact on the system. Then recalculate the contribution of distinct variables to the overall system failure by Eq. 8 for analysis: CONZ ðnÞ ¼

Xa i¼1

conzðm; nÞ

ð8Þ

The analysis of the change rate contribution graph proposed in this paper can sharply capture the variables that are more critical to the impact of the entire system. Firstly, the method excludes the variables of the cooperative change to reduce the data interference of the analysis, and the second aspect can simultaneously pay attention to the influence of different variables on the stability of the system at different times. 3.2

Dynamic Likert Scale

Revised Likert scale (RLS) is a collaborative summary score format for surveys. Respondents typically use rankings from high to low depending on quality or performance. Using the contribution of each variable as a scoring basis can not only consider the degree of effect of different variables on the system, but also dynamically adjust the variable priority according to system changes. The dynamic RLS establishment steps are as follows: 1. Setting the Likert table grade L according to the particular situation, and the relevant scale takes the integer value 1 to L.

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2. Rate each variable proportionally based on the contribution level obtained at each moment. 3. For each variable, use the sliding window to take the first 100 sampling points at the current time and count the frequency of different scores under the current sliding window of different variables. 4. Based on the results of the third step, use Eq. 9. to calculate the ultimate score for each variable: P MSm ¼

fml  dl ; ð0  MSi  L; m ¼ 1; 2; . . .N Þ L

ð9Þ

Where MSi indicates the average score of properties m, dj indicates the score of each sample, f ml represents the frequency of the attribute m scored as dl , L represents the data length. 5. Normalize the current time score MSi using Eq. 10: Zm ¼

MSm  l d

ð10Þ

According to the EEMUA standard, the percentages of low, medium and high variables are separately 80%, 15%, 5%. When the data is too positive, the threshold of the corresponding ratio is 0:8r and 1:5r. That is, the score is less than 0:8r for the C-level alarm; Score is greater than 0:8r and less than 1:5r is a B-level alarm; greater than 1:5r is an A-level alarm variable. The dynamic Likert scale score proposed in this paper can reasonably divide the priority of each variable on the basis of the contribution graph analysis when the fault occurs. On the other hand, when the system has distinct faults, the method can dynamically adjust the priority of each variable.

4 Case Study The Tennessee Eastman Process which is created by the Eastman Chemical Company aim to evaluating process control and process monitoring. In the TE process, XMEAS (1–41) are 41 observed variables, XMV (1–11) are 11 control variables, and a total of 52 variables are used for fault analysis. In the standard TE data set, the normal data set has a total of 500 sampling points, IDV (1–21) is 21 fault data sets, respectively, there are 960 sampling points in each fault condition, and the fault is introduced from the 160th sampling point. In the TE process, XMEAS (1–41) is 41 observed variables, XMV (1–11) is 11 control variables, and a total of 52 variables are used for fault analysis. In the standard TE data set, the normal working condition data set has a total of 500 sampling points, and the IDV (1–21) is 21 fault data sets, each having 960 sampling points, wherein the fault is introduced from the 160th sampling point.

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IDV (1) is a stepwise fault caused by XMEAS (4) to introduce a step change in the overall system. Since XMEAS (4) is the feed ratio of A\C, and XMEAS (1) is the feed of A, therefore there is a strong relationship between the two, there is a compensation mechanism, the variable fluctuations of XMEAS (1) and XMEAS (4) are shown in Fig. 2:

XMEAS(1)

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Fig. 2. The measured value of XMEAS (1) and XMEAS (4) in IDV (1)

When the fault occurs, the whole process generates a dynamic fault, which gradually stabilizes after the 500th sampling point, and the whole system no longer has dynamic faults. The traditional Likert scale method and the present contribution graphLiKert scale method. The Figs. 3(a) and (b) are the result, respectively. Among them, deep red represents the highest priority variable at the current time; Orange represents the priority second variable; Yellow represents the relatively unimportant variable under the current fault.

(a)

(b)

Fig. 3. Based on the traditional Likert scale prioritization (a) and contribution map Likert scale priority (b)

As can be seen from the analysis in Fig. 3(a), the traditional Likert scale method can get the importance of different variables, but due to the large number of alarms simultaneously, the scores of each variable are very close, resulting in uncertainty and rating. The real-time performance is poor. In contrast to the results of Fig. 2(b), the contribution graph-Likert scale method proposed in this paper effectively captures the correlation and fluctuation of XMEAS (1) and XMEAS (4). At different times, the contribution of each variable is different, so that the variables at different times have different priorities. In addition, the method proposed in this paper has nice Dynamic.

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In summary, the contribution graph-LiKert scale method proposed in this paper can not only generate alarms to prioritize each variable when multiple alarms occur at the same time, but also have certain auxiliary traceability in single variable alarms effect. For the traditional Likert scale method, when the system state changes, the priority of each variable cannot be adjusted in real time, and the state change of the system variable cannot be known according to the historical alarm amount. The method of this paper has excellent performance in both real-time and rationality.

5 Conclusions Based on previous studies, this paper constructs a dynamic model with more accurate prioritization. First, the traditional 0–1 alarm value is replaced by the value of the contribution, so that the information in the calculation of the priority division is retained more. After that, the model adds 3r2 filtering based on the contribution of traditional PCA, which makes the calculation of contribution more reasonable. Secondly, this paper proposes a method of dynamic prioritization using sliding windows to continuously adjust the variable level according to data and system priority. Compared with the traditional prioritization, the model can analyze the degree of influence of each variable on the stability of the system, and analyze the alarm variables in real time. The validity of the method is proved by TEP. It is of great significance to the alarm management in the actual industry. In addition, because the contribution of PCA cannot accurately deal with nonlinear faults, in later research, the analysis method involving coupling and nonlinear data is the key research content. Acknowledgements. This research is supported by the National Natural Science Foundation of China under Grant Nos. 61573051 and 61473026 and Fundamental Research Funds for the Central Universities under Grant Nos. JD1914 and XK1802-4.

References 1. Chang YJ, Khan FI, Ahmed S (2011) A risk-based approach to design warning system for processing facilities. Process Saf Environ Prot 89(5):310–316 2. Zhang YT, Wang HQ, Cheng GP (2015) Associated alarm grouping and suppression strategy based on process data. Oil Gas Chem Ind 44(5):100–104 (in Chinese) 3. Zhao H (2016) Process alarm rationalization setting method and its application. Chem Autom Instrum 43(11):1208–1210 4. Geng ZQ, Zhu QX, Gu XB (2005) A fuzzy clustering–ranking algorithm and its application for alarm operating optimization in chemical processing. Process Saf Prog 24(1):66–75 5. Foong OM, Sulaiman S (2009) Alarm prioritization system for oil refinery. In: Proceedings of the world congress on engineering and computer science, San Francisco 6. Gao HH, Xu Y, Gu X, Lin X, Zhu QX (2015) Systematic rationalization approach for multivariate correlated alarms based on interpretive structural modeling and likert scale. Chin J Chem Eng 23(12):1987–1996 7. Zhu XX, Braatz R (2014) Two-dimensional contribution map for fault identification. Control Syst IEEE 34(5):72–77

Research on Optimization of Intelligent Assignment of Crane Task Priority Hexu Sun, Pengcheng Wang, and Zhaoming Lei(&) Hebei University of Technology, Tianjin 300130, China [email protected]

Abstract. The collision problem that occurs during the scheduling process of the crane is generally solved by prioritizing In order to intelligently solve the problem of task prioritization in the process of crane scheduling, the task prioritization model is established by using support vector machine (SVM). Aiming at the difficulty of parameter setting in the process of SVM, a dynamic cuckoo search (DCS) algorithm is proposed. The algorithm optimizes the parameter setting of SVM. In order to solve the shortcomings of the cuckoo algorithm, the step size and discovery probability of the standard cuckoo algorithm are improved as the dynamic adaptive step size and dynamic discovery probability. The test function verifies that it has the advantages of fast convergence speed and strong local optimal ability. Finally, the DCS-SVM is used to establish the prioritization model. After experimental verification, the method is proved to be effective, and it can accurately and intelligently solve the priority division problem of the crane scheduling task. Keywords: Priority


SVM
Cuckoo search algorithm
Dynamic adaptation

1 Introduction The crane, also known as the bridge crane, is an large-scale equipment for steel companies to transport materials. Collision between cranes often occurs during the dispatching process of the overhead crane. Generally, the priority of the mission is used to solve the problem of crane collision. Literature [1] uses the Max-heap method to classify the priority of user privacy protection from a multi-user perspective. The method used is more traditional and simple, mainly considering the efficiency of priority division and the protection of privacy; The literature [2] uses the review level analysis to legally evaluate and prioritize the execution of the project, but it is not possible to quantitatively divide and calculate the priority. In [3], BP neural network is used to construct the classification model of maintenance task priority. Based on the data-driven deep learning method, the priority classification model can be obtained by mining the data, which can better solve the priority classification problem, but the model parameters. For random settings, it is easy to fall into local optimum and still continue to improve. In this paper, the dynamic cuckoo search algorithm (DCS) is used to optimize the initial parameters of SVM, and the DCS-SVM is used to establish the prioritization model of the crane scheduling task, © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 456–464, 2020. https://doi.org/10.1007/978-981-32-9050-1_52

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and compared with PSO-SVM and SVM classifier. It proves that DCS-SVM has higher accuracy and can effectively solve the problem of prioritization of crane scheduling tasks.

2 Support Vector Machine SVM is a machine learning method based on statistical learning theory proposed by Vapick et al. in the 1990s. It is widely used in solving pattern recognition problems in small sample, nonlinear and high dimensional feature spaces. The basic idea of SVM is to map the input data that is linearly inseparable in the original space to the highdimensional space by using the kernel function, that is, the nonlinear mapping function, so that the optimal classification surface of the input data can be found in the highdimensional space, so that the final result is available. The optimal classification surface function is: f ðxÞ ¼ signð

N X

ai yi Kðx xi Þ þ bÞ

i¼1

ð1Þ

0\ai \C In the formula, ai means the lagrangian multiplier; b 2 R means the judgment threshold that is calculated according to the support vector; C is the penalty factor that is used to control the classification interval and the classification error; KðxTi xÞ represents the kernel function, it is often used to have a polynomial kernel function. In the actual application process, the RBF kernel function has the characteristics of less parameters, nonlinear mapping, fast convergence, etc. Therefore, this paper uses the RBF kernel function, and its expression is:   xi  xj 2 Kðxi ; xj Þ ¼ expð Þ ð2Þ 2r2 Where, r [ 0 is the bandwidth control parameter of the RBF kernel function affects the radial range of the function. The implementation process of SVM is as follows: (1) The training set is T ¼ fðx1 ; y1 Þ; ðx2 ; y2 Þ; . . .; ðxi ; yi Þg, in the formula, i ¼ 1; 2; . . .; N; xi 2 Rd ; yi 2 f1; 1g; (2) Select the kernel function Kðxi ; xj Þ and the penalty factor C to find the dual problem of convex quadratic programming: maxð a

s:t:

N X i¼1

N X i¼1

ai 

N X N 1X ai aj yi yj Kðxi xj ÞÞ 2 i¼1 j¼1

ai yi ¼ 0; ai 2 ½0; C 

ð3Þ

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 T (3) Find the optimal solution a ¼ a1 ; a2 ; . . .; aN from Eq. (3), select the component of a that is not zero, and calculate the decision threshold by the following formula: b ¼ y j 

N X i¼1

  ai yi K xi
xj

ð4Þ

(4) Based on a and b establish the optimal classification surface function: f ðxÞ ¼ signð

N X i¼1

ai yi Kðx xi Þ þ b Þ

ð5Þ

In the SVM algorithm, two parameters of the penalty factor C and the kernel function bandwidth r need to be determined. Manual adjustment may lead to a decrease in the accuracy of classification results. Therefore, this paper uses the dynamic adaptive cuckoo algorithm to optimize the calculation of two parameters to improve the classification accuracy of the trained model. The SVM was originally designed to solve the two-class problem, but in the actual application process, the binary problem is only part of it, and there are many multiclassification problems. At present, there are two main methods for solving multiclassification problems on using SVM. In experimental applications, Hsu and Lin [4] are verified by experiments on different data sets. Several multi-classification methods are compared. The experimental results show that the one-to-one [5] method in the indirect method works better.

3 Dynamic Cuckoo Search Algorithm 3.1

Cuckoo Search Algorithm

Cuckoo search algorithm (CS) is a new intelligent search algorithm proposed by Yang et al. [6] in 2009. There are three hypotheses: (1) Each bird randomly places the unique egg produced in a host nest; (2) The nest containing the finest eggs will be preserved to the next generation; (3) The number of nests that can lay eggs is fixed, and the overview of bird eggs found by the host is Pa 2 ½0; 1. When discovered by the host, the host may choose to abandon the bird eggs or rebuild the nest. The cuckoo nest position update formula under three assumptions is: xti þ 1 ¼ xti þ d  LevyðbÞ

ð6Þ

In the formula, xti is the position of the i nest selected by the algorithm in the t iteration; d the amount of step control used to control the size of the step movement, usually expressed by the formula d ¼ d0 ðxti  xbest Þ, where d0 is a constant, generally

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d0 ¼ 0:01, which xbest is the current optimal solution;  represents point-to-point multiplication; LevyðbÞ represents the walking step that obeys the levi distribution, and its expression is: LevyðbÞ ¼

/ l jvj1=b

; 1\b\3

ð7Þ

Where, l; v subject to the standard normal distribution, i.e. l  Nð0; 1Þ; v  Nð0; 1Þ; the parameter b ¼ 1:5, / obtained by the formula (3.26): 0 /¼

1b1 Cð1 þ bÞ sinðpb=2Þ @ A C½ð1 þ bÞ=2b 2

b1 2

ð8Þ

In the formula, C is a standard Gamma function. 3.2

Dynamic Cuckoo Search Algorithm

It is known through experiments that the accuracy of the CS algorithm needs to increase the number of nests. The disadvantage of this method is that the convergence speed of the algorithm will decrease. At the same time, the CS algorithm is also easy to fall into local optimum, resulting in inaccurate optimization results. Aiming at the above problems, this paper improves the cuckoo algorithm, and adopts two methods, namely dynamic adaptive step size and dynamic adaptive discovery probability, to improve the convergence speed of the algorithm, the ability to jump out of local optimum and the precision of optimization. Dynamic Adaptive Step Size. Through the research and analysis of the algorithm, it is found that the random update step size of Levi’s flight strategy can’t better guide the population to the direction of the optimal solution. Therefore, this paper changes the random step size to the dynamic adaptive step size, which is expressed as: g ¼ gmin þ hðgmax  gmin Þ tc h¼1 tmax

ð9Þ

Where, g is the decision parameter for the levi flight step; gmax ; gmin is the range variable, which represents the maximum and minimum values of the parameter; h is the control parameter related to the number of iterations, which represents the current iteration number and maximum the number of iterations. According to the analysis, Eq. (9) can change the step control factor of levi flight in the interval ½tc ; tmax  due to the existence of control parameters h. As the number of iterations increases, the value of g gradually decreases, thus adjusting the movement of levi flying. The distance makes the algorithm adaptive.

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Dynamic Adaptive Discovery Probability. The size of Pa affects the diversity of the population in the iterative process, the convergence speed of the algorithm, and the optimization precision. The fixed size cannot respond differently according to the change of the number of iterations. Therefore, the probability of finding the fixed value is changed to dynamic. Adapt to the discovery probability, expressed as: Pda ¼ Pa;max sin



 p Tc  1
þ Pa;min 2 Tmax  1

ð10Þ

In the formula, Tc ; Tmax represents the current iteration number and the maximum number of iterations respectively; Pa;max ; Pa;min is the range control parameters respectively represented by the range of Pa are gradually increased when the number of iterations gradually increases, because the sinusoidal increment strategy in the equation is nonlinear growth, and the value in the previous period is small. It can guarantee the update frequency of the bird’s nest in the iterative process of the algorithm, maintain the diversity of the population, perform fast search iteration, and speed up the convergence speed of the algorithm. In the later stage, the value is large and the variation range is small, which can ensure that the population has a fixed update frequency, but not too fast, to ensure the local optimization accuracy of the algorithm, improve search efficiency, and quickly get the optimal solution.

4 Dynamic Cuckoo Algorithm Optimizes Support Vector Machine The SVM algorithm needs to determine the parameters ðC; rÞ. If the parameters are improperly selected, the classification results will be inaccurate. Therefore, this paper uses DCS to optimize the parameters ðC; rÞ of the SVM, and iteratively updates the parameters as the nest position of the DCS, and uses the classification accuracy as the fitness function. In summary, the steps of DCS-SVM is as follows: (1) Initialization algorithm parameters: population size setting, total iteration number, solution dimension, probability of the host discovering the bird’s egg, range of the solution, whether it falls into the local optimal judgment variable, etc.; then initialize the above-mentioned set population size in a random manner N nest location: X ¼ ðx1 ; x2 ; . . .; xn ÞT ; (2) Calculating the fitness value and record the current optimal solution: Solve the fitness value of the individual in the population through the objective function, select the optimal fitness value after the calculation is completed, and record the position of the optimal individual; (3) Updating other nest locations with dynamic adaptive step sizes; (4) Using the objective function to solve the fitness value of the newly produced individual, and compare the fitness values of the new and old nests. If the fitness value of the newly generated nest is better than the previous generation, replace the old individual with the new one;

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(5) Generating a uniformly distributed random number r (between 0–1) and compare the found probability of the bird’s egg. The calculation formula is calculated using the improved publicity (10), if r [ Pa , a new solution is generated for the discovered bird egg replacement; (6) Solving the fitness value of the entire population, select the global optimal value, and record its location; (7) The number of iterations or the solution precision is judged. If the corresponding conditions are met, the iteration is stopped, the obtained global optimal solution is output, the training samples are trained, and the classifier is constructed; otherwise, the Step 3–Step 6 steps are repeated.

5 Case Analysis 5.1

Data Processing

Based on the investigation and analysis of the actual crane scheduling plan of a steel company in Hebei, this paper summarizes the six factors that have an impact on the crane lifting task. They are: total amount of goods, profit contribution rate, remaining scheduling time, deferred usage time, task execution time, and number of task execution steps, wherein the remaining scheduling time, deferred usage time, task execution time, and task execution steps are prioritized. The effect of the level is negatively correlated, so the reciprocal is used. The input space is multi-dimensional data, and there are differences in dimension and magnitude between different dimension data. Therefore, the input data is normalized by min-max method, and the formula is: x0 ¼

x  xmin ; x0 2 ½0; 1 xmax  xmin

ð11Þ

In this paper, 150 sets of crane scheduling data of Hebei iron and steel enterprises in recent years were selected as training data. Among them, the priority was divided into 3 levels, and each level of data was 50. Table 1 shows 10 pieces of data after data preprocessing. S1  S6 represent the Total amount of goods, profit contribution rate, purchase frequency, cooperation cycle, task execution time, number of task execution steps. Experiments were carried out using the parameters obtained by the optimization, and the classification prediction results of the test samples were randomly selected as shown in Figs. 1, 2 and 3. The classification prediction results of DCS-SVM, PSOSVM and SVM models are respectively indicated. According to the preliminary classification results, Figs. 1, 2 and 3 shows that among the 50 test sample data, the prediction number of DCS-SVM is 47, the prediction accuracy is 94%, and the prediction accuracy of PSO-SVM is 45. The prediction accuracy rate is 90%, while the fixed parameter SVM model has only 42 correct predictions and the prediction accuracy rate is 84%, as shown in Table 1.

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Fig. 3. SVM priority prediction classification result

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Table 1. 10 normalized sample data No. 1 2 3 4 5 6 7 8 9 10

S1 0.84 0.81 0.66 0.49 0.76 0.89 0.54 0.73 0.80 0.88

S2 0.89 0.84 0.65 0.44 0.81 0.91 0.51 0.73 0.75 0.92

S3 0.64 0.51 0.29 0.13 0.56 1.00 0.17 0.40 0.62 0.61

S4 0.67 0.37 0.21 0.10 0.42 0.77 0.14 0.32 0.40 0.47

S5 0.52 0.35 0.23 0.12 0.36 0.55 0.17 0.34 0.40 0.47

S6 0.87 0.46 0.23 0.12 0.47 0.89 0.11 0.35 0.72 0.59

Priority 3 2 1 1 2 3 1 2 3 3

Table 2 shows the parameters obtained by the optimization of the CS algorithm and the PSO algorithm and the model parameters of the three models of the parameters originally set by the SVM.

Table 2. Comparison of three model parameters. Optimization model C r DCS-SVM 65.44 0.071 PSO-SVM 13.28 0.53 SVM 1 0.17

After performing a 3-fold cross-experimental verification on the sample data, the obtained three-time classification accuracy rate is arithmetically averaged, and the results are shown in Table 3.

Table 3. 3-fold crossover experiment verification result. Optimization model 1 2 3 DCS-SVM 94% 96% 92% PSO-SVM 90% 88% 90% SVM 84% 86% 86%

Average accuracy 94.0% 89.3% 85.3%

6 Conclusion In order to solve the task priority determination problem in the crane scheduling, this paper establishes the priority division model of DCS optimized SVM model parameters ðC; rÞ, and tests the DCS algorithm with the international common test function. The

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experiment verifies that the improved algorithm has convergence speed. Fast, jump out of the local optimal ability and other advantages, establish a DCS-SVM model and compare with PSO-SVM, SVM and other models. The experimental results show that the DCS-SVM model can effectively solve the priority division problem of the crane dispatching task and has broad application prospects.

References 1. Wang G, Lu R, Guan YL Achieve privacy-preserving priority classification on patient health data in remote eHealthcare system. IEEE Access. https://doi.org/10.1109/access.2019. 2891775 2. Choi JS (2015) Improvements of efficiency of analytical hierarchy process (AHP) for project priority in international rural development 27(1):7–14 3. Lv X, Fan BX, Yin J, Wang XW (2014) a priority classification method for maintenance tasks based on BP neural network. Comput. Eng. Appl. 50(24):250–254 4. Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425 5. Scholkopf B, Burges CJC, Smola AJ (1999) Advances in kernel methods: support vector learning. MIT Press, Cambridge, pp 255–268 6. Yang XS, Deb S (2010) Engineering optimization by cuckoo search. Int J Math Model Numer Optimisation 1(4):330–343 7. Hu W, Yang MY (2016) Neural network modeling based on niche cuckoo search algorithm. Comput Simul 33(12):309–313

Optimization of Vehicle Scheduling Within the Steel Enterprises Based on IAGSO Algorithm He-xu Sun, Fan Zhao, and Zhaoming Lei(&) Hebei University of Technology, Tianjin 300000, China [email protected]

Abstract. At present, the vehicles in the steel enterprises of China mainly use the traditional one-to-one transportation of the trucks. The mode, which has low transportation efficiency, high transportation cost and long time to stop at the task node, affects the production process. To solve this, it is proposed to adopt the “trailer-semi-trailer” cycle-to-carry transport mode in the factory, and establishes an integer programming model with the least number of tractors and total logistics cost, and then use the improved Glowworm Swarm Optimization algorithm to solve the model. The adaptive strategy improvement algorithm makes it increase its own search ability, and at the same time introduces the immune mechanism of Immune Algorithm, enhances the ability of the algorithm to jump out of local optimum, and finally tests through examples. The results show that the improved algorithm can quickly find a better scheduling scheme, thus guiding the enterprise to carry out the in-plant transportation reasonably and efficiently. Keywords: Swap trailer transport
Glowworm Swarm Optimization Adaptive strategy
Immune Algorithm




1 Introduction Good vehicle transportation scheduling solution can not only reduce logistics costs but also improve production efficiency. China’s research and application of swap trailer transport are relatively late. The literature [2] gives a detailed explanation of the theoretical basis and operational form of the swap trailer transport, and proposes relevant policies and recommendations that promote the swap trailer transport of China. In order to meet the problem of swap trailer transport under the aging requirements, the literature [3] shows that the optimization of sling tractor scheduling can produce good energy saving and emission reduction effects under the time window requirement. The literature [4] puts forward the swap trailer transport form of using the hanging transportation in the factory, and establishes the scheduling model of the swap trailer transport in the enterprise and the actual performance evaluation system, and proposes the related methods to improve the transportation performance. This paper adopts the summary and analysis of the swap trailer transport schedule of the predecessors, combined with the problems existing in the © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 465–472, 2020. https://doi.org/10.1007/978-981-32-9050-1_53

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transportation of vehicles in the steel enterprise between the various mission nodes, the use of “tractor-semi-trailer” vehicle circulation in the factory mode.

2 Problem Description and Mathematical Modeling 2.1

Problem Description

The cyclical swap trailer transport scheduling can be described as follows: At the beginning of the dispatch, each transport node has prepared a sufficient number of semi-trailers and the goods that need to be dispatched have been loaded, and the tractors are driven out of the yard to each task node to suspend the semi-trailer. As shown in Fig. 1, A, B, C, D, and E represent a task node, O represents the yard, and the direction of the arrow is the direction of transportation. There are six tasks. At the beginning, the goods that are shipped to other nodes at A, B, C, D and E need to be completed. Then the tractor starts to access a task node from the yard O and is sent to the corresponding unloading task node after assembling the semi-trailer, and then continues to complete other transportation tasks. If the existing vehicle cannot complete the transportation of all tasks within the specified time, other vehicles are dispatched from the yard to ensure the smooth completion of the mission, requiring minimum dispatch and minimum mission cost.

B

C A

E

O D

Fig. 1. Schematic diagram of the problem of swap trailer transport in the factory

The task time window is defined as a hybrid time constraint, and the tractor is required to reach its task loading node before the latest start time of the specified task. The tractor can arrive at the task loading node earlier than the earliest start time required by the mission but must pay some penalty fee. 2.2

Mathematical Model

According to the above assumptions and descriptions, the construction of the swap trailer transport scheduling model and constraints are as follows:

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( min P ¼ min

C P n P n P

minfC g ) M P lc dij Xijc þ w maxðETm þ tZm a  RZm ; 0Þ

c¼1 i¼0 j¼0 C P

467

ð1Þ

m¼1

ymc ¼ 1

ð2Þ

ðc ¼ 1; 2; . . .; CÞ

c¼1 C P

ymc pm þ g  Qc

ð3Þ

ðc ¼ 1; 2; . . .; CÞ

c¼1 n P

Xojc ¼ 1

ðc ¼ 1; 2; . . .; CÞ

ð4Þ

j¼0 n P

Xjkc 

j¼0

n P

Xkic

ðk ¼ 1; 2; . . .; n; c ¼ 1; 2; . . .; CÞ

ð5Þ

i¼0 n P

Xioc

ðc ¼ 1; 2; . . .; CÞ

ð6Þ

i¼0

tm  PdZvm Xmy þ 2ts 2c mc

ðm ¼ 1; 2; . . .; MÞ

ð7Þ

c2V

tmf ¼ tmk þ tm tmk  LTm tc;h þ 1;k  tc;h;f

ðm ¼ 1; 2; . . .; MÞ

ð8Þ

ðm ¼ 1; 2; . . .; MÞ

ðc ¼ 1; 2; . . .; C; h ¼ 1; 2; . . .;

ð9Þ M P

ymc  1Þ

ð10Þ

m¼1

M is the number of tasks in a certain period of time; pm is traffic of task m; Zm ; Xm respectively indicate the loading point and unloading point of task m; C is the number of tractors; Qc is trailer quality of tractor c; g is Alignment quality of semi-trailer; dij is the distance between task nodes i and j, i 6¼ j; dii ¼ 0; dij ¼ dji ; doj is the distance between task nodes i and parking lot; tia is the average loading and unloading waiting time of the vehicle at task node i; ts is working hours of swap trailer transport; RZm is the time when the tractor arrives at the loading point of the task m; tmk is the start time of task m, that is, the time when the tractor starts to haul the semi-trailer at Zm ; tmf is the end time of task m, that is, the time when the tractor is hanging under the Xm ; tm is task duration; tc;n;k is the tractor c at the beginning of the n th mission; tc;n;f is the end of the n th mission; ½ETm ; LTm  is indicates the time window at which task m begins; v1c is the average speed of the factory when the tractor is empty; v2c is average speed in the factory when the tractor is loaded; w is penalty factor; l1c is tractor c no-load transportation cost coefficient; l2c is tractor c load transportation cost coefficient; pm  0 and is an integer. lc is the different transportation cost factor when no-load load, l1c indicates the cost of no load, l2c indicates the cost of the load.

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Decision variables:  Xijc ¼  ymc ¼

1 0

1 0

tractor c directly from i to j else task m is completed by tractorc else

The objective function (1) consists of two parts, requiring the minimum number of vehicles and total cost. Constraint (2) means that each task can only be completed by its corresponding tractor. The constraint (3) means that the tractor cannot be towed beyond the limit, and the constraint (4–6) means that the tractor is departed from the yard and finally returns, the constraint (7) is the continuous constraint of the task, the constraint (8) is the end time of the task, the constraint (9) is the time window constraint of the task start, constraint (10) requires the tractor to proceed to the next task after completing a task.

3 Adaptive Glowworm Swarm Optimization Algorithm Based on Immune Mechanism 3.1

Adaptive Strategy

We propose Glowworm Swarm Optimization [5] algorithm with adaptive mechanism. In the early iteration period, the step size is appropriately increased. As the number of iterations increases, the individual’s moving step size is reduced by the neighborhood density. The individual neighborhood density and moving step size formulas are: densityti

jNi ðtÞj p  rdi2

sti þ 1 ¼ sti þ densityti  sti 

ð11Þ nt  jNi ðtÞj p  rdi2

ð12Þ

densityti is the neighborhood density of the individual i in the t th generation. 3.2

Immune Mechanism

Immune Algorithm which can not only maintain the scale of excellent antibodies but also prevent the lack of diversity of antibody populations [6]. The matching relationship between the objective function and the solution represented by the individual is represented by affinity, and the calculation formula of the affinity magnitude is as shown in the formula (13). The excellent individuals in the iterative process are stored through the memory retention mechanism to ensure that excellent individuals always exist and avoid population degradation. Through the concentration adjustment mechanism to increase the diversity of the population, inhibit

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the high-compatibility and high-concentration individuals while ensuring the presence of high-complexity and low-concentration individuals, and the low-compatibility and low-concentration individuals will not disappear and exist in a small amount. The vector moment based method of (14) calculates the antibody concentration. The probability selection formula shown in Eq. (15) is used to suppress individuals with high fitness and high concentration to enhance population diversity. In order to increase population diversity while preventing population degradation, individuals were vaccinated using formula (16): gðxi Þ ¼ af ðxi Þ þ ð1  aÞd ðxi Þ M  P 

d ð xi Þ ¼

ð13Þ

  f ð xi Þ  f xj 

j¼1 M P M  P 

  f ð xi Þ  f x j 

; i ¼ 1; 2;


; M:

ð14Þ

i¼1 j¼1

PðiÞ ¼ a

f ð xi Þ d ðiÞ þ ð 1  aÞ N N P P f ð xi Þ d ðiÞ i¼1

ð15Þ

i¼1

Xi ðtÞ ¼ Xbest þ rand  RðtÞ

ð16Þ

gðxi Þ indicates the affinity value of individual I, d ðxi Þ indicates the concentration of the i-th individual, f ðxi Þ is the fitness function of individual i, a is weight coefficient. Xi indicates the position of the individual i after vaccination, Xbest indicates the vaccine selected from the current vaccine library, RðtÞ represents the search radius of the t th generation of the individual i. Individuals who are vaccinated will be randomly concentrated around the optimal population.

4 Experiment Analysis 4.1

Experimental Parameters and Data

For the convenience of description, the letters A, B, C, D, E, F, G, H, I, J, and K are used to indicate the loading and unloading points of the transportation tasks in the factory, and O is the in-plant parking lot. The detailed data of the tractors and trucks that can be dispatched by the yard are shown in Table 1.

Table 1. Vehicle specific data. Car type Qc ðtÞ v1c ðkm=hÞ v2c ðkm=hÞ l1c ðRMB=kmÞ l2c ðRMB=kmÞ ts ðminÞ Tractor 35 40 15 1.0 2.0 3 Lorry 40 40 20 1.0 2.0

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If the vehicle type is a truck, the average loading and unloading time of the truck at each mission node is shown in Table 2. Table 2. Average task waiting time for trucks. Task point Average task time (min) Task point Average task time (min) A 20 G 20 B 20 H 30 C 20 I 15 D 25 J 20 E 30 K 25 F 25

The dispatching requirement contains a total of 20 transportation tasks, all of which are required to start within the time window [9:00–10:00]. The loading, unloading point number and volume of each task are given in Table 3. Table 3. Average task waiting time for trucks. Mission 1 2 3 4 5 6 7 8 9 10

Zm A D F F F F F A A J

Xm F F J J I I H J I B

pm 35 27 30 30 26 26 30 32 28 26

Mission 11 12 13 14 15 16 17 18 19 20

Zm J I I C C H H B B K

Xm K G C H H B B K E E

pm 29 34 35 25 25 24 24 30 33 28

Apply the above example data to the established mathematical model, and use the improved firefly optimization algorithm to solve the scheduling model. The initial values of the algorithm are shown in Table 4. The population number N is 50, the maximum number of iterations is 200, and the number of randomly generated individuals is M. The number of inoculated individuals R is 5, and the penalty coefficient in the model is set to 0.6. Table 4. Initial parameter list. q c b nl l0 s 0.4 0.6 0.08 5 5 0.3

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Run the program 20 times, take the best solution as the final solution. Through the output result of the final solution, the scheduling scheme of the cyclical transport can be obtained. As shown in Table 5, the specific start time and end time of the transport task are shown by Gantt chart 3, wherein the abscissa is the time course, with 8 points The starting point of the abscissa, the ordinate represents the vehicle number (Fig. 2). 5

13

Vehicle number

4

15

9

3

0

19

7

20

8

10

4

20

10

17

18

3

11

1

16

12

2

2

14

1

30

5

40

6

50

60

Time/min

Fig. 2. Gantt chart of the swap trailer transport plan Table 5. Swap trailer transport plan. Vehicle number 1 2 3 4

Driving path O-J-K-E-F-H-AF-I-F-I-O O-D-F-J-F-J-B-O O-A-I-G-B-K-AJ-O O-I-C-H-C-H-BH- B-E-O

Load path (m) 2910

Empty path (m) 1375

Stop time (min) 36

Driving time (min) 20

3600 5365

3870 4790

24 24

23 32

3150

1530

36

19

Taking the tractor 2 as an example to illustrate the driving process of the tractor, the tractor starts from the yard at 8:00 and goes to the mission node D. After the semitrailer is towed, the mission point is reached at 8:11. After unloading the trailer at point F, drag on another trailer ready to arrive at the mission point at 8:19. After the trailer is removed, the mission point F is reached again at 8:23, and the mission point is reached at 8:28 after being towed. After unloading the trailer, drag another trailer ready to arrive at mission point 8 at 8:41. Tractor 2 completed all missions at 8:44 and began to return to the yard. 4.2

Contrast Results

For the in-plant transportation tasks, the comparison of the various indicators of the bicycle scheduling mode and the cyclic suspension scheduling mode is shown in Table 6. As can be seen from the above table, the circulation time-carrying transportation has

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higher utilization rate and mileage utilization rate. The scheduling of trucks has been greatly improved while the driving cost has been significantly reduced. Table 6. Comparison of transportation methods. Evaluation index Utilization rate of departure time Utilization rate of mileage Driving cost

Truck scheduling 10.93%

Swap trailer transport 44.08%

Increase percentage 33.15%

34.19% 58.97

59.06% 41.62

24.87% −41.69%

5 Conclusion In order to solve the problems of traditional factory vehicle transportation, a swap trailer transport model was established, and combined with a transportation example of a steel company in Hebei, the improved Glowworm Swarm Optimization Algorithm was used to solve the problem. The effectiveness of the proposed swap trailer transport model and the improved algorithm is proved by comparing with the truck scheduling mode and other algorithms.

References 1. Liang F, Liang GQ (2017) Design and simulation of the target location of logistics distribution vehicle scheduling in internet of vehicle. Comput Simul 34(4):377–381 2. Li YR (2004) Semi-trailer swap transport: an effective way to improve road transport efficiency. J Highw Transp Res Dev 04:119–122 3. Li HQ, Zhao WC, Li YR (2016) Trailer pick-up tractor routing problem with timeliness requirement and solving. J Traffic Transp Eng 16(5):95–102 4. Zhang ZY (2013) Research on circulated dropping and pulling transportation and performance applied in large manufacturing enterprise. Chang’an University 5. Wang X, Yang K, Zhou X (2018) Two-stage glowworm swarm optimisation for eco-nomical operation of hydropower station. IET Renew Power Gener 12(9):992–1003 6. Wang Y, Geng X, Zhang F et al (2018) An immune genetic algorithm for multi-echelon inventory cost control of IOT based supply chains. IEEE Access PP(99):1

Research on Wind Power Optimization Scheduling Based on Improved Plant Growth Simulation Algorithm Hexu Sun, Hang Zhang, and Zhaoming Lei(&) Hebei University of Technology, Tianjin 300130, China [email protected]

Abstract. To ensure the safety, stability and economic operation including wind power systems, the system constructed to minimize the operating cost of the spinning reserve capacity optimization target scheduling model. To enhance the computing speed for solving the model, an improved plant growth simulation algorithm is proposed to optimize this model. The reverse learning idea is introduced into the plant growth algorithm, and the growth point is inversely mutated to expand the search space of the algorithm; the intelligent variable step search is adopted and the variation mechanism of the elite set ensures fast optimization and improves the accuracy of the solution. Finally, the example verification is carried out on the IEEE30 bus system. Experimental results show that the proposed model can efficiently solve the wind farm containing spinning reserve capacity optimization scheduling problem, has broad application prospects. Keywords: Wind power system
Optimized scheduling plant growth simulation algorithm Opposition-based learning
Intelligent step change




1 Introduction The optimization of power system with large-scale wind power is one of the research hot spots in the field of new energy power generation and power system optimization [1]. However, the volatility and randomness of wind power output make it difficult for traditional deterministic scheduling schemes to meet the uncertainty requirements brought by wind power. As the size of the unit increases, the traditional accurate algorithm takes a long time to calculate and is prone to “dimensionality disasters” [2]. Around this issue, experts and scholars have concentrated on a large number of indepth research on wind power output uncertainty modeling and unit optimization scheduling. The literature [3] uses the genetic algorithm to solve, but the dependence on the parameters is large. Once the parameters are set improperly, it will have a great influence on the solution results. In this paper, the system standby deviation cost and the abandonment cost brought by wind power uncertainty are taken into account in the total operating cost, and the mathematical model of power system optimization scheduling with wind farm is © Springer Nature Singapore Pte Ltd. 2020 Z. Deng (Ed.): CIAC 2019, LNEE 586, pp. 473–481, 2020. https://doi.org/10.1007/978-981-32-9050-1_54

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established with the goal of minimum total cost. The traditional simulated plant growth algorithm is improved in the ability to optimize the speed and jump out of the extreme points, and the improved algorithm is used to solve the model.

2 Optimized Scheduling Mathematical Model 2.1

Symbol Description

For ease of description, the introduction of symbols is defined as follows: F is the totally operating cost of the power system; F1 is the conventional unit costs, including operation, start-stop and backup costs; F2 is the abandonment costs; F3 is the alternate deviation costs; I is the number of conventional generator sets; T is the total time periods in the scheduling; fi ðpti Þ is the Power generation cost for the unit during the period t; uti is the start and stop state of the unit during the period t; pti is the active power; D ai ; bi ; ci are the generator fuel cost factor; SU i and Si are the cost and cost of start and stop U D of unit i; di and di are the positive and negative spare cost factors provided to the unit i; D RU i;t and Ri;t are the positive and negative spare capacity of the unit; N is the number of wind turbines; wtn is the abandoned wind power for the first wind turbine during the period t; dn is the abandoned wind cost factor; dtU and dtD are the deviations of the D positive and negative spare capacities during the period t; nU t and nt are the weighting t factor; ew is the wind power prediction error; pn;r is the active power observation; ptn;w is the wind power active power prediction value; W N is the wind farm rated installed capacity; Dt is the total load during time period t; PtL is the total network loss of the system; ridown and riup are the maximum landslide and climbing ability allowed by the active output respectively; pimin and pimax are the upper and lower limits of the unit on off and Ti;t1 are the continuous opening and stopping time of the unit from output; Ti;t1 on off the end to t − 1; Ti;min and Ti;min are the minimum opening and closing time of the unit respectively; M is the maximum number of starts and stops allowed by the unit during the scheduling period; Prfg is the probability operator; b is the confidence level. 2.2

Model Establishment

Establish a mathematical model for optimal scheduling of wind power systems: min F ¼ F1 þ F2 þ F3 F1 ¼

I X T X i¼1 t¼1

ð1Þ

I X T I X T     X   U X   U U D D Si þ uti fi pti þ uti 1  ut1 uit1 1  uti SD i i þ di Ri;t þ di Ri;t i¼1 t¼1

i¼1 t¼1

ð2Þ      2 fi pti ¼ ai þ bi pti þ ci pti

ð3Þ

Research on Wind Power Optimization Scheduling

F2 ¼

N X T X n¼1 t¼1

T X

F3 ¼

t¼1

wtn dn

D D U nU t dt þ nt dt

475

ð4Þ

ð5Þ

ew ¼ ptn;r  ptn;w

ð6Þ

ew  Nðl; rÞ

ð7Þ

1 1 N W r ¼ ptn;w þ 5 50

ð8Þ

Equation (1) is the objective function, including the conventional unit operation and start-stop cost, the abandonment cost and the rotating reserve capacity deviation cost; the formula (2) is the general unit operation total cost; (3) is the unit power generation cost function; formula (4) is the abandonment cost; formula (5) is the rotation reserve deviation cost; formula (6) is the wind power prediction error function, which satisfies the normal of Eqs. (7)–(8) distributed. 2.3

Restrictions

uti

I X i¼1

pti þ

N X n¼1

ðptn;w  wtn Þ ¼ Dt þ PtL

ð9Þ

 riup Dt ridown Dt  pti  pt1 i

ð10Þ

uti pimin  pti  uti pimax

ð11Þ

 8  on on < Ti;t1  Ti;min  uti  0 ut1 i    : T off  T off 0 uti  ut1 i;t1 i;min i

ð12Þ

8 N T    PP t > U t t > u p þ R þ e þ p < w D i imax i;t n;w n¼1 t¼1    N P T  P > > t t : uti pimax  RD i;t þ pn;w þ ew  D

ð13Þ

n¼1 t¼1

T X t¼1

juti  ut1 i jM

ð14Þ

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8 I N P P > t t U > ðptn;w  wtn Þ þ ew  Dt g  b < Prfui pi þ Ri;t þ i¼1

n¼1

i¼1

n¼1

I N P P > > : Prfuti pti  RD ðptn;w  wtn Þ þ ew  Dt g  b i;t þ

ð15Þ

Equation (9) is the power balance constraint; (10) is the unit climbing slope constraint; (11) is the upper and lower limit of the generator set operation; Eq. (12) is the minimum opening and closing time constraint of the unit; (13) is the system backup constraint; (14) is the stop number constraint; (15) is the reserve deviation constraint.

3 Improved Plant Growth Simulated Algorithm 3.1

Original Plant Growth Simulated Algorithm

In recent years, the Plant Growth Simulation Algorithm (PGSA) has shown strong stability, global search ability and accuracy in the fields of integer programming and optimization scheduling [4]. The growth point contains a chemical growth hormone called morphactin, and the concentration of the morphactin corresponding to each growth point on the stem and the stem is PMi ¼ ðPM1 ; . . .; PMp Þ and Pmi ¼ ðPm1 ; . . .; Pmq Þ. The formula for calculating the morphactin concentration value is: PMi ¼

p P i¼1

f ðSMi Þ  f ðx0 Þ q     P ðf ðSMi Þ  f ðx0 ÞÞ þ f Smj  f ðx0 Þ j¼1



Pmj ¼

p P i¼1

ð16Þ



f Smj  f ðx0 Þ q     P ðf ðSMi Þ  f ðx0 ÞÞ þ f Smj  f ðx0 Þ

ð17Þ

j¼1

The random number falls into the morphactin concentration [0, 1] state space as shown in Fig. 1. When there are multiple growth points to be grown, the growth point with large morphactin concentration has the priority growth right, that is, the closer to the light source The higher the fitness of the growth point, the greater the probability of being selected.

random number

0.0

Pk+q

{

P2

{ {

P1

1.0

Fig. 1. State space of morphactin

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The plant growth simulated algorithm has no complex parameters such as coding rules and mutation rates. The possibility of not finding the global optimal solution due to improper parameter selection is low [5]. But the optimization efficiency may be reduced when solving large problems. So the original algorithm is further improved. 3.2

Opposition-Based Learning

Opposition-Based Learning (OBL) is an effective method which has been proved that the inverse solution has a probability of nearly 50% better than the current solution. Introducing the idea of reverse learning into PGSA and proposing an improved plant growth simulated algorithm (IPGSA). 0

Xi;t ¼ k ðAi þ Bi Þ  Xi;t ; Xi;t 2 ½Ai ; Bi 

ð18Þ

0

Where Xi;t is the generalized inverse solution of Xi;t ; k is the random number in the range ½0; 1. The solution mechanism of reverse learning can enrich the diversity of growth points in the point set, enhance the ability to jump out of the local extremum. 3.3

Intelligent Variable Step Strategy

The value of the optimization step can directly affect the optimization result. Therefore, changing the fixed step size to the intelligent dynamic search step size can be expressed as: k ¼ k0 ð1 

IN  1 Þ þ kmin Imax  1

ð19Þ

Where k is the search step size, k0 is the initial step size, kmin is the minimum search step size, d is the step parameter, IN is the current number of iterations, and Imax is the maximum number of iterations. By dynamically changing the value of the step size, IPGSA can quickly search for the optimal solution using a larger step size at the beginning of the iteration, improved the convergence speed to a certain extent; use a smaller step size to improve the optimization accuracy later in the iterative process. Perform a more precise search near the current better solution. The solution flow chart for the entire scheduling plan is (Fig. 2):

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H. Sun et al. Start Initialize parameters to determine initial scheduling scheme X0 Update the growth point to solve the SMi and calculate the morphogen concentration PSMi Find the inverse solution S`Mi, calculate PS`Mi

ΔP=PSMi-PS`Mi > Cgen ¼ UGj aj þ bj PGj þ cj PtGj þ > > > j¼1 t¼1 <  h  i UGt j 1  UGt j g0j þ g1j 1  es=sj > > > NH T P > P > > rHk StHk PHk DT : Chighload ¼

ð11Þ

ð12Þ

t¼1 k¼1

Where: Cgen is the power generation cost of conventional units, including operating costs and start-stop costs; NG is the number of conventional units; PtGj and UGt j are the active output and start-stop status variables of the conventional unit j in t periods, UGt j ¼ 0 means Stop, UGt j ¼ 1 means Open; s is the normal unit shutdown time; aj , bj , cj and g0j , g1j , sj are the operating cost parameters and start and stop cost parameters of the conventional unit j; NH is the number of groups of high load capacity; NH is the unit adjustment cost of the k high load capacity; StHk is the switching state of the k high load capacity in the t period, StHk ¼ 0 represents interrupted operation, StHk ¼ 1 represents putting into operation; PHk is the unit switching capacity of the k high-capacity; DT is the duration of the t period. 3.2

Constraints

(1) System Constraints. Including power balance constraints and rotation reserve constraints. 8 NG NW NH P P P > > > UGt j PtGj þ PtWi ¼ PtL þ StHk PHk > > > j¼1 i¼1 k¼1 > >