Proceedings of 2020 Chinese Intelligent Systems Conference: Volume I [1st ed.] 9789811584497, 9789811584503

Citation preview

Lecture Notes in Electrical Engineering 705

Yingmin Jia Weicun Zhang Yongling Fu   Editors

Proceedings of 2020 Chinese Intelligent Systems Conference Volume I

Lecture Notes in Electrical Engineering Volume 705

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 Laboratory, Karlsruhe Institute for Technology, Karlsruhe, 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 Martín, 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 Laboratory, 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, 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

The book series Lecture Notes in Electrical Engineering (LNEE) publishes the latest developments in Electrical Engineering - quickly, informally and in high quality. While original research reported in proceedings and monographs has traditionally formed the core of LNEE, we also encourage authors to submit books devoted to supporting student education and professional training in the various fields and applications areas of electrical engineering. The series cover classical and emerging topics concerning:

• • • • • • • • • • • •

Communication Engineering, Information Theory and Networks Electronics Engineering and Microelectronics Signal, Image and Speech Processing Wireless and Mobile Communication Circuits and Systems Energy Systems, Power Electronics and Electrical Machines Electro-optical Engineering Instrumentation Engineering Avionics Engineering Control Systems Internet-of-Things and Cybersecurity Biomedical Devices, MEMS and NEMS

For general information about this book series, comments or suggestions, please contact leontina. [email protected]. To submit a proposal or request further information, please contact the Publishing Editor in your country: China Jasmine Dou, Associate Editor ([email protected]) India, Japan, Rest of Asia Swati Meherishi, Executive Editor ([email protected]) Southeast Asia, Australia, New Zealand Ramesh Nath Premnath, Editor ([email protected]) USA, Canada: Michael Luby, Senior Editor ([email protected]) All other Countries: Leontina Di Cecco, Senior Editor ([email protected]) ** Indexing: The books of this series are submitted to ISI Proceedings, EI-Compendex, SCOPUS, MetaPress, Web of Science and Springerlink **

More information about this series at http://www.springer.com/series/7818

Yingmin Jia Weicun Zhang Yongling Fu •

•

Editors

Proceedings of 2020 Chinese Intelligent Systems Conference Volume I

123

Editors Yingmin Jia School of Automation Science and Electrical Engineering Beihang University Beijing, Beijing, China

Weicun Zhang School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing, Beijing, China

Yongling Fu School of Mechanical Engineering and Automation Beihang University Beijing, Beijing, China

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-15-8449-7 ISBN 978-981-15-8450-3 (eBook) https://doi.org/10.1007/978-981-15-8450-3 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are solely and exclusively licensed 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

Distributed Finite-Time Rotating Encirclement Control for Second-Order Agent Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yaxin Li, Yingmin Jia, and Tengfei Zhang

1

Urban Road Object Detection and Tracking Applications Based on Acoustic Localization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhimin Wang, Chaoli Wang, and Song Shen

10

Air Combat Situation Assessment of Multiple UCAVs with Incomplete Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shouyi Li, Qingxian Wu, Mou Chen, and Yuhui Wang

18

Detection and Depth Estimation for Objects from Single Monocular Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ziwen Xu and Yingmin Jia

27

Leader-Following Consensus of Multi-agent Systems: New Results Based on a Linear Transformation Approach . . . . . . . . . . . . . . . . . . . . Jingyuan Zhan and Yangzhou Chen

36

Proportional Step Perturbation Method MPPT for Boost Circuit of TEG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Feng Ji and John Xu

48

Leader-Following Consensus of Second-Order Networks with a Moving Leader and Nonconvex Input Constraints . . . . . . . . . . . Lipo Mo, Zeyang Xu, and Yongguang Yu

57

A Novel Deep Learning Ensemble Model with Secondary Decomposition for Short-Term Electricity Price Forecasting . . . . . . . . . Na Chen, Xueqing Yang, Yiming Gan, Wuneng Zhou, and Hangyang Cheng

69

v

vi

Contents

Disturbance Observer-Based Finite-Time Control for Systems with Nonlinearity and Disturbance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xinqing Li and Xinjiang Wei

78

Disturbance Observer-Based Disturbance Attenuation Control for Dynamic Positioning System of Ships . . . . . . . . . . . . . . . . . . . . . . . . Lihong You and Xinjiang Wei

88

Multiple Change Points Detection Method Based on TSTKS and CPI Sliding Window Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jialun Liu, Jinpeng Qi, Junchen Zou, and Houjie Zhu

99

Steam Pressure Control Based on PLC 200 Smart . . . . . . . . . . . . . . . . . 110 Jing Lv and Zhongsuo Shi Event-Triggered Anti-disturbance Tracking Control for Systems with Exogenous Disturbances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Xiaoli Zhang, Xiang Gu, Yang Yi, and Tianping Zhang Parallel Label Consistent KSVD-Stacked Autoencoder for Industrial Process Fault Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Hongpeng Yin, Jiaxin Guo, Guobo Liao, and Yi Chai Iterative Learning Formation Control for Multi-agent Systems with Randomly Varying Trial Lengths . . . . . . . . . . . . . . . . . . . . . . . . . . 136 Yimin Fan, Yang Liu, and Na Wang Backstepping-Based Adaptive Neural Control of Constrained Nonlinear Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Penghao Chen, Tianping Zhang, Houbin Qian, and Yang Yi A Music Generation Model Based on Generative Adversarial Networks with Bayesian Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Yijie Xu, Xueqing Yang, Yiming Gan, Wuneng Zhou, Hangyang Cheng, and Xuehui He Interactive Attention and Position-Aware Mechanism for Aspect-Level Sentiment Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Xuehui He, Yiming Gan, Xueqing Yang, Wuneng Zhou, and Yuanhong Ren Neural Network-Based Exponential Stability of Affine Nonlinear Systems by Event-Triggered Approach . . . . . . . . . . . . . . . . . . . . . . . . . 175 Fan Liu, Yiming Gan, Xueqing Yang, and Wuneng Zhou Adaptive Finite-Time Leader-Following Consensus of Multi-agent Systems Against Time-Varying Actuator Faults . . . . . . . . . . . . . . . . . . . 185 Yanhui Yin, Fuyong Wang, Zhongxin Liu, and Zengqiang Chen

Contents

vii

Domain Decomposition Strategies for Developing Parallel Unstructured Mesh Generation Software Based on PadMesh . . . . . . . . 194 Fengshun Lu, Xiong Jiang, Xinbiao Bao, Long Qi, and Yongheng Guo Dynamic Trajectory Prediction for Continuous Descend Operations Based on Unscented Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206 Jun Zhang, Guoqing Wang, and Gang Xiao Research on Weighted Multiple Model Adaptive Control Based on U-Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 Jiayi Li and Weicun Zhang Human Action Recognition Method Based on Video-Level Features and Attention Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Qiang Cai, Jin Yan, Haisheng Li, and Yibiao Deng Application of LSTM in Aeration of Sewage Treatment . . . . . . . . . . . . 234 Shaobo Zhang and Qinglin Sun Sliding Mode Control on Coordination of Master-Slave Manipulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242 Lijun Wang, Ningxi Liu, Jinkun Liu, Tianyu Cao, and Jiaxuan Yan Pose Ambiguity Elimination Algorithm for 3C Components Assembly Pose Estimation in Point Cloud . . . . . . . . . . . . . . . . . . . . . . . 251 Weikun Gu, Xiansheng Yang, Dengwei Dong, and Yunjiang Lou Manipulator Control Law Design Based on Backstepping and ADRC Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261 Lijun Wang, Jiaxuan Yan, Tianyu Cao, and Ningxi Liu Manipulator Calibration-Free Hand-Eye Coordination Based on ADRC Under Eye Fixation . . . . . . . . . . . . . . . . . . . . . . . . . . . 270 Lijun Wang, Tianyu Cao, Jiaxuan Yan, and Ningxi Liu Adaptive Neural Consensus Tracking for Second-Order Nonlinear Multi-agent Systems with Full-State Constraints . . . . . . . . . . . . . . . . . . 278 Dan Liu and Lin Zhao Adaptive Sliding Mode Control of Mismatched Quantization System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 Qiaoyu Chen, Wuneng Zhou, Dongbing Tong, and Yao Wang An Optimal Feedback Control Law for Spacecraft Reorientation with Attitude Pointing Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 Bin Li, Yang Wang, and Kai Zhang Active Detection Based Mobile Robot Radioactive Source Localization Method and Data Processing . . . . . . . . . . . . . . . . . . . . . . . 308 Han Gao, Zhengguang Ma, and Yongguo Zhao

viii

Contents

Distributed Adaptive Consensus Tracking Control for Multiple AUVs with State Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 Jingzi Fan and Lin Zhao Intelligent Wireless Propagation Model with Environmental Adaptability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326 Xiaoyu Qu and Jiangyun Wang The Local Navigation and Positioning System of Unmanned Ground Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 Shaowei Li, Qingquan Feng, Jiangang Wang, Zhiyong Li, Dongxiao Wang, and Shizhao Liu A Novel 3D Lidar-IMU Calibration Method Based on Hand-Eye Calibration System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342 Lei Ji, Long Zhao, Jingyun Duo, and Chao Wang Design of Semi-physical Simulation System for Multi-target Attack Air Defense Missile Weapon System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 Shujun Yang, Jianqiang Zheng, Qinghua Ma, Shuaiwei Wang, Jirong Ma, Haipeng Deng, and Yiming Liang Application of Multi-network Fusion in Diagnosis of Chest Diseases . . . 358 Shanshan Zhang and Hai Gao Distributed Robust H1 Containment Control for Fractional-Order Multi-agent Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Xiaolin Yuan, Yongguang Yu, and Lipo Mo Refinement and Validation of Humoral Immunity Based on Event-B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377 Xuqing Shi, Shengrong Zou, Yudan Shu, and Li Chen Accelerated Distributed Algorithm for Solving Linear Algebraic Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389 Weikang Hu and Aiguo Wu CNN-Based Automatic Diagnosis for Knee Meniscus Tear in Magnetic Resonance Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 Hao Zhou, Liyan Zhang, Bing Zhang, Juan Wang, and Chengyi Xia Application of an Effective Fault Localization Prioritization Method to Stereo Matching Software . . . . . . . . . . . . . . . . . . . . . . . . . . . 409 Jinfeng Li, Yan Zhang, and Jilong Bian Denoising of X-Ray Pulsar Signal Based on Variational Mode Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 Yong Zhao, Yingmin Jia, and Qiang Chen

Contents

ix

Signal Estimation of Fatigue-Magnetic Properties of 25CrMo4 Based on Stein Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 428 Zhenfa Bi and Guobao Yang Intelligent Frequency Selection of the Sky-Wave Radar Based on Numerical Ray Tracing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 Runze Li, Jiangyun Wang, and Guanghong Gong Design of Multi-port Energy Conversion System of Electric Vehicle Based on Bridge-Type Buck-Boost Topology . . . . . . . . . . . . . . . . . . . . . 446 Yunhao Zhang, Xiaonan Xia, Xiaoxing Ge, Wei Tang, and Yu Fang Realization of Automatic Zero Calibration of Inductive Proximity Sensor Based on Inductive Increment Detection . . . . . . . . . . . . . . . . . . . 455 Yuyin Zhao, Yu Fang, Jiajun Yang, Miao Weng, and Xiaonan Xia Sliding Mode Control Method of Powered Parafoil Based on Extended State Observer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464 Li Yu, Qinglin Sun, and Panlong Tan Analysis of Accelerated Vibration-Magnetic Effect of 25CrMo4 . . . . . . . 475 Zhenfa Bi and Zongkai Wang Automated Prediction of Cervical Precancer Based on Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485 Bing Zhang, Qingyuan Zhang, Hao Zhou, Chengyi Xia, and Juan Wang Dynamic Economic Dispatch Considering Wind Based on Adaptive Crisscross Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495 Panpan Mei, Lianghong Wu, Hongqiang Zhang, and Zhenzu Liu Consensus for Heterogeneous Networked Systems Based on Second-Order Neighbors’ Information . . . . . . . . . . . . . . . . . . . . . . . 508 Lei Wang, Huanyu Zhao, Dongsheng Du, and Hongbiao Zhou Sliding Mode Control for Neutral-Type Systems with Stochastic Noises and Time-Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 518 Qiaoyu Chen, Wuneng Zhou, and Dongbing Tong Reinforcement Learning Adaptive Tracking Control for a Stratospheric Airship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527 Kang Wang, Yang Liu, Zewei Zheng, and Ming Zhu Model Reference Adaptive Control with Output Constraints . . . . . . . . . 541 Yu Hua, Tianping Zhang, Manfei Lin, and Weiwei Deng Low-Dose CT Image Denoising Using a Generative Adversarial Network Based on U-Net Network Structure . . . . . . . . . . . . . . . . . . . . . 549 Yuan Fang, Guoli Wang, Xianhua Dai, and Xuemei Guo

x

Contents

Robust Monocular Visual-Inertial SLAM Using Nonlinear Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 560 Jingyun Duo, Lei Ji, and Long Zhao Research on Aerodynamically Assisted Orbit Maneuver Method Based on Feature Model Correction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569 Yue Lin, Yingmin Jia, and Songtao Fan Fault Estimation of Switched Linear Systems with Actuator and Sensor Faults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579 Chunying Su, Jianting Lyu, Xin Wang, and Dai Gao Deep Convolutional Neural Network for Real and Fake Face Discrimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 590 Yuanyuan Li, Jun Meng, Yaqin Luo, Xinghua Huang, Guanqiu Qi, and Zhiqin Zhu Control Design for One-Sided Lipschitz Nonlinear Systems with Actuator Saturation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 599 Lin Yang, Jun Huang, and Haoran Zhang Stochastic Stability of Itô Stochastic Systems with Semi-Markov Jump . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 608 Min Zhang, Jun Huang, and Haoran Zhang Agility Detector Designed for Automobile Detection . . . . . . . . . . . . . . . . 615 Shuang Liu, Xizhong Shen, and Rongfan Leo Review of Model Predictive Control Methods for Time-Delay Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 624 Lei Liu, Yi He, and Cunwu Han Resistivity Inversion Solving Based on a GA Optimized Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 634 Peng Wang and Shurong Li GECNN-CRF for Prostate Cancer Detection with WSI . . . . . . . . . . . . . 646 Jinfeng Dong, Xuemei Guo, and Guoli Wang Design Method of Robot Welding Workstation Based on Adaptive Planing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 659 Haofei Dai, Zhaojiang Liu, Yizhong Luan, Jiyang Chen, Wenxu Sun, and Sile Ma Road Intersection Path Planning Based on Q-learning for Unmanned Ground Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 669 Lingxue Zhao, Chaofang Hu, Yao Guo, and Patrick Tjan Univariate ReLU Neural Network and Its Application in Nonlinear System Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 679 Xinglong Liang and Jun Xu

Contents

xi

Development of Multiply Magnetic Field Generator Combined with Living Cell Workstation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 688 Jiansheng Xu, Chuanfang Chen, Deyu Kong, Linfei Ye, and Ming Xu TIO Loss: A Transplantable Inversed One-Hot Loss for Imbalanced Multi-classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 696 Lin Wang and Chaoli Wang Image Classification Method Based on Generative Adversarial Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 708 Longhui Hu and Chaoli Wang Design and Development of Integrated Device for Wireless Detection of Flue Gas in Cremation Equipment . . . . . . . . . . . . . . . . . . . 721 Fengguang Huang, Lin Tian, and Wei Wang A Hierarchical Fuzzy Comprehensive Evaluation Algorithm for Running States of a Mine Hoist Synchronous Motor Drive System (MHSS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 730 Wei Liu, Fuzhong Wang, Ao Hou, and Sumin Han Disturbance Observer-Based Design and Analysis of Iterative Learning Control with Nonrepetitive Uncertainties . . . . . . . . . . . . . . . . 739 Zirong Guo and Deyuan Meng On Scaled Consensus, Bipartite Consensus and Scaled Bipartite Consensus: A Unified Viewpoint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 749 Yuxin Wu and Deyuan Meng Voltage Balancing of Modular Multilevel Converter Based on Cerebellar Model Articulation Controller . . . . . . . . . . . . . . . . . . . . . 760 Xiangsheng Liu, Yuanyuan Yang, Lin Ren, Zhengxin Zhou, Yunxia Jiang, Lailong Song, and ZhengLin Jiang Consistency of Continuous Multi-agent Systems with Privacy Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 769 Meiyan Yu, Hongyong Yang, Yujiao Sun, and Fei Liu Short-Term Prediction of Photovoltaic Power Generation Based on Deep Belief Network with Momentum Factor . . . . . . . . . . . . . . . . . . 778 Lai Lei, Jiangzhen Guo, Fuzhong Wang, and Li Zhang Improved Adaptive Filter with Unknown Process and Measurement Noise Covariance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 792 Jirong Ma, Yumei Hu, Qinghua Ma, Shujun Yang, Jianqiang Zheng, and Shuaiwei Wang Extended State Observer-Based Sliding Mode Control for Epilepsy . . . 801 Wei Wei, Ping Li, and Min Zuo

xii

Contents

Research on General System Level Training Simulation Technology of Mid-And High-End Military UAV . . . . . . . . . . . . . . . . . . . . . . . . . . . 810 Qing Zhang, Jiahui Tong, and Haifeng Li Event-Based Robust State and Fault Estimation for Stochastic Linear System with Missing Observations and Uncertainty . . . . . . . . . . 819 Zhidong Xu, Bo Ding, and Tianping Zhang Sliding Mode Control for a Constant Force Suspension System . . . . . . . 832 Yuxin Jia, Yingmin Jia, Kai Gong, Yao Lu, and Meng Duan Real-Time Coverage Path Planning of a UAV with Threat and Value Zone Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 840 Yan Liu, Hao Li, and Zhi Liu Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 849

Distributed Finite-Time Rotating Encirclement Control for Second-Order Agent Dynamics Yaxin Li, Yingmin Jia(B) , and Tengfei Zhang The Seventh Research Division and the Center for Information and Control, School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing 100191, China [email protected] Abstract. Rotating encirclement control problem is a important part in coordinated control and are widely used. In this paper we will solve this problem for second-order agents in finite-time. We establish three estimators for each agent to gauge the geometric center, polar radius and polar angle firstly. The reference trajectory for each agent will be obtained by the estimated values, which will make all agents be uniformly distributed on the circumference at the same speed. Then we use the control law to make all agents follow a predetermined trajectory, which ensure that in finite time, the agents uniformly encircle the targets. The simulation results are also showed to prove the feasibility of the methods. Keywords: Encirclement control estimator · Multi-agent

1

· Finite-time stable · Distributed

Introduction

Recently, multi-agent technology has developed rapidly, and coordinated control has attracted a great deal of interest from a large number of experts and scholars. Coordinated control problems can be used for UAV formations, robot rescue and so on. Enclosing control of multi-agent system is an achievement in the coordinated control, which has great application value and significance. D. V. Dimarogonas et al. proposed the enclosing control of multi-agent in [1]. A definition of multi-agent surrounding control is given in [4]. A method of finite-time rotation encirclement control for first-order systems with non convex input constraints is proposed in [2]. [5] solved the problem of surrounding control involving a group of leaders and followers. A control schemes for nonholonomic mobile robots is designed in [7] by only using the relative bearing measurements. Encirclement control for multiple leaders which are stationary or dynamic are investigated in [8]. Collective rotations for second-order multi-agents are investigated in [6] with the help of Lyapunov theory for complex systems. [3] proposed a method which aims to analyze finite time stability. [9,10] investigated the containment control under switching topology. c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Singapore Pte Ltd. 2021 Y. Jia et al. (Eds.): CISC 2020, LNEE 705, pp. 1–9, 2021. https://doi.org/10.1007/978-981-15-8450-3_1

2

Y. Li et al.

The organization of this paper is as follows: basic theory about graph theory, encirclement control, and finite-time stability is described in Sect. 2. Control design will be shown in Sect. 3. And the simulation of our method will be shown in Sect. 4. Our conclusion is shown in Sect. 5.

2

Preliminaries

In this section, basis theory of graph theory, stability in finite-time, encirclement control with second-order agent dynamics will be present. 2.1

Graph Theory

Supposed that G(V, E, A) is an undirected graph of order n, V is the set of nodes, E is the set of edges, and A = [aij ] ∈ Rn×n is the weighted adjacency matrix. If / E, aij = 0, for all i ∈ V aii = 0. (i, j) ∈ E, aij = aji > 0 and if (i, j) ∈ The neighbor set of node i is defined as Ni = {j ∈ N : (i, j) ∈ E}. The graph Laplacian induced by the information flow G is defined as L(G) = [lij ], where n lij = −aij for all i = j, lii = j=1 . 2.2

Stability in Finite-Time

Definition 1. The system can be considered as follows x˙ = f (x), x ∈ Rm

(1)

If fi (r1 x1 , . . . , rm xm ) = σ+ra fa (x),  > 0, i = 1, . . . , m, then we can called the continuous vector field f (x) is homogeneous, and σ ∈ R is called degree, r = (r1 , ..., rk ) is called dilation. If f (x) is homogeneous, then we can get that the system (1) is homogeneous. Lemma 1. Provided that in system (1), f is continuous and is homogeneous with degree σ and dilation (r1 , ..., rk ), its asymptotically stable equilibrium is x = 0. If degree σ < 0, then the equilibrium of system (1) can stabilize in finite-time. Lemma 2. Provided that V: Rn → R+ ∪ {0} is positive definite function with a continuous radially unbounded such that 1) V (x) = 0 ⇒ x ∈ M 2) V˙ (x) ≤ −(αV p (x) − βV q (x))b , p > 0, q > 0, alpha > 0, β > 0, b = 1, pb < 1, qb > 1. then n the settling time the system is finite-time stable, and ∀x ∈ R  , T (x0 ) represents  and it satisfies T (x0 ) ≤ 1/αk (1 − pk) + 1/β k (qk − 1) . 2.3

Encirclement Control with Second-Order Agent Dynamics

We consider each node of the graph is a dynamic agent x˙i = vi , v˙i = ui , i ∈ V

(2)

Rotating Encirclement Control for Second-Order Agent Dynamics

3

where xi ∈ R2 donates the position information of agent i, vi ∈ R2 donates the velocity information of the i-th agent, ui ∈ R2 donates the control input of the i-th agent. In order to express this problem more directly, we introduce the polar coordinate transformation, which can be expressed: xi (t) = c(t) + [ri (t)cosθi (t), ri (t)sinθi (t)]T

(3)

where c(t) donates the geometric pa (t) denotes the position mcenter of all targets, 1 2 p (t) ∈ R . r (t) donates the polar radius, of the a-th target, c(t) = m i a=1 a which means the distance from c(t) to pa (t). θi (t) donates the polar angle, in rectangular coordinates, it is expressed as the angle between the positive direction of the X axis and the connection between the pa (t) and c(t). In this paper, we can’t get the specific information of the c(t), ri (t) and θi (t), so we need to estimate these values only by the neighbor’s information. We give an example to show the polar coordinate expression more vividly in Fig. 1.

Fig. 1. Encirclement control problem

Definition 2. For all i ∈ V, if system (2) and (3) meet conditions (4) by a control law ui (t) in a finite time T > 0, then the system can achieve finite-time rotating encirclement control. ⎧ 2π(i − j) ⎪ ⎪ =0 ⎪ θi (T ) − θj (T ) − ⎨ n (4) ri (T ) − kl(T ) = 0 ⎪ ⎪ ⎪ ⎩˙ θi (T ) − δ = 0 where l(t) = maxa∈T { pa (t) − c(t) }, k > 1, the desired angular velocity is represent by δ. Assumption 1. l(t) ≤ l∗ , l∗ is a constant. The speeds of all targets satisfies p˙a (t) ≤ p∗ , a ∈ T .

4

2.4

Y. Li et al.

Notations

In this paper, we consider the encirclement problem with n agents and m targets. NjV ⊆ V is a set of agents, whose elements can get the information of target j. |NiT | donates the quantity of elements in NjV . NiT ⊆ T donates the set of targets whose information can be obtained by the agent i. x donates the Euclidean norm, where x ∈ Rn is a real vector. Assumption 2. The graph is connected in this paper. |NiT | ≥ 1, ∀j ∈ T .

3

Main Results

In this section, we designd three estimators for ci (t), ri (t), θi (t). We will design a reference trajectory for by these estimated value. Stability analysis is also given. 3.1

Estimation of the Geometric Center(c(t))

In this part, we will establish the distributed estimator for all agents to obtain the estimated geometric center c˜i (t). ⎧ ηij (t) = c˜i (t) − c˜j (t), j ∈ Ni ⎪ ⎪ ⎪ ⎪ ηij (t) ⎪ ⎪ ⎪ ⎨ φ˙ i (t) = −αi1 − αi2 ηij (t) ηij (t) ηij (t) (5) j∈Ni j∈Ni ⎪ ⎪ ⎪ 1 n ⎪ ⎪



⎪ ⎪ c˜i (t) = φi (t) + m

N V pj (t) ⎩ j∈NiT

j

where φi (t) ∈ R2 is a vector, φi (0) = 0. αi1 , αi2 are positive design parameters. Using estimators (5), we analyze the finite-time stability of the estimation of the geometric center. Lemma 3. Using Assumptions 1. We choose that αi1 > (n − 1)p∗ , αi2 > 0, ∀i ∈ V, c˜(t) will stabilize to c(t) in finite-time. n Proof. Define c¯(t) = n1 k=1 c˜k (t), we choose a Lyapunov function. n 1  T [˜ ci (t) − c¯(t)] [˜ ci (t) − c¯(t)] ≤ p(t)2 2 i=1 2 n

V1 (t) =

ci (t) − c˜j (t) }. where p(t) = maxi,j∈V { ˜ ⎧ ⎡ n ⎨ ηij (t) T V˙ 1 (t) = [˜ ci (t) − c¯(t)] ⎣−αi1 ⎩ ηij (t) i=1 j∈Ni

−αi2

j∈Ni

⎤⎫ ⎬ 1 n

p˙j (t) − c¯˙(t)⎦ ηij (t) ηij (t) +

N V

⎭ m j T j∈Ni

(6)

(7)

Rotating Encirclement Control for Second-Order Agent Dynamics

5

Using Assumption 1, Assumption 2, and Lemma 2, we can get that ⎧ ⎫ n ⎨ ⎬ η (t) ij T [˜ ci (t) − c¯(t)] −αi1 ⎩ ηij (t) ⎭ i=1 j∈Ni

=− =−

αi1 2 αi1 2

n



T

[˜ ci (t) − c¯(t)]

i=1 j∈Ni n

n ηij (t) αi1 T ηij (t) − [˜ ci (t) − c¯(t)] ηij (t) 2 i=1 ηij (t) j∈Ni

T

[˜ ci (t) − c¯(t)]

i=1 j∈Ni

αi1 ηij (t) + ηij (t) 2

n

T

[˜ cj (t) − c¯(t)]

i=1 j∈Ni

n αi1 ηij (t)T ηij (t) =− ≤ −αi1 s(t) 2 i=1 ηij (t)

ηij (t) ηij (t) (8)

j∈Ni

⎫ ⎧ n ⎨ ⎬ −4α i2 T ηij (t) ηij (t) ≤ s(t)3 −αi2 [˜ ci (t) − c¯(t)] 4 ⎭ ⎩ n i=1

(9)

j∈Ni

⎧ ⎪

n n ⎨ T [˜ ci (t) − c ¯(t)] m i=1 ⎪ ⎩

 j∈NiT

⎫ ⎪ ⎬

1

  p˙ (t) ≤  V j ⎪ ⎭ Nj 

(n − 1) m

max

i,j∈V

n    ηij (t) i=1 j∈N T i

p∗

  = (n − 1)p∗ s(t)  ν Nj 

(10) According to Eqs. (8)–(10),

√ √ 2 (αi1 − (n − 1)p∗ ) 12 4αi2 2 2αi2 3 √ V˙ 1 (t) ≤ − [αi1 − (n − 1)p∗ ] p(t) − 4 p(t)3 ≤ − V1 − 5 √ V12 n n n n

(11) According to the Lemma 2, we can get that the estimated geometric center c˜i (t) will converge to√ the real geometric center c(t) with a finite-time T1 , √ n5 2n 2n satisfying that T1 ≤ αi1 −(n−1)r . ∗ + 2αi2 d

3.2

Estimation of the Polar Radius ri (t)

In this part, we will establish the distributed estimator for all agents to obtain r˜i (t) the polar radius which means the estimated polar radius. ζi1 (t) r˜˙i1 (t) = −βi1 − βi2 ζi1 (t) |ζi1 (t)| , ζi1 (t) = r˜i1 (t) − max (zij (t)) |ζi1 (t)| j∈NiT ζi2 (t) − βi2 ζi2 (t) |ζi2 (t)| , ζi2 (t) = r˜i2 (t) − max (˜ rj1 (t)) r˜˙i2 (t) = −βi1 |ζi2 (t)| j∈Ni ∪(i) .. .   ζiD (t) − βi2 ζiD (t) |ζiD (t)| , ζiD (t) = r˜iD (t) − max r˜j(D−1) (t) r˜˙iD (t) = −βi1 |ζiD (t)| j∈Ni ∪{i] riD (t) r˜i (t) = κ˜

(12)

6

Y. Li et al.

where βi1 , βi2 are positive design parameters. zij (t) = pj (t) − c˜i (t) , D = max d(i, j) Using a Lyapunov function and the Lemma 2, we can get that the polar radius θi (t) with estimated polar radius r˜i (t) will converge to√the expected √ 2M 2M a finite-time T2 , satisfying that T2 ≤ T1 + 2βi2 + (βi1 −2p∗ ) . 3.3

Estimation of the Polar Angle θi (t)

In this part, we will establish the distributed estimator to get θ˜i (t) for all agents. ⎧ ξij (t) ⎪ ˜˙i (t) =−γi1 ⎪ θ a ξ (t) |ξ (t)| + δ(t) − γ aij ⎪ ij ij ij i2 ⎨ |ξij (t)| j∈Ni j∈Ni (13) ⎪ ⎪ 2π(i − j) ⎪ ⎩ ξij (t) =− + θ˜i (t) − θ˜j (t) n where γi1 , γi2 are positive design parameters. Using a Lyapunov function and the Lemma 2, we can get that the estimated polar angle θ˜i (t) will converge to the expected polar angle θi (t) with a finite-time T3 , satis