Distributed Localization and Formation Control of Multi Agent Systems with Local (eng)

This book mainly focuses on the key technologies and important issues related to localization estimation and formation control for networked multi-agent systems. Chapter 1 introduces the concepts of multi-agent systems, fundamental issues in localization and formation, research progress both at home and abroad, and the theory of system stability. Chapters 2–8 present research results on distributed localization estimation under conditions such as random deployment, non-ideal channels, random noise, varying-trajectory length, sensor multiplicative failure, and local bearing measurement. Chapter 9 focuses on integrated research progress in localization estimation and formation control for multi-agent systems under local perception conditions. The book demonstrates the effectiveness and superiority of the theoretical methods through simulation and physical examples.

Yunkai Lv received the B.Sc. degree in measurement and control technology and instrument, the M.Eng. degree in control science and engineering from Qingdao University of Science and Technology, Qingdao, China, in 2016 and 2019, respectively, and the Ph.D. degree in control science and engineering from Tongji University, Shanghai, China, in 2023. He is currently Postdoctoral Fellow with the School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China. His research interests include network-based control, distributed localization and formation, and multi-agent systems.

Huaicheng Yan received his B.Sc. degree in automatic control from Wuhan University of Technology, China, in 2001, and the Ph.D. degree in control theory and control engineering from Huazhong University of Science and Technology, China, in 2007. From 2007 to 2009, he was Postdoctoral Fellow with the Chinese University of Hong Kong. Currently, he is Professor with the School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China. He has published over 400 papers in the peer-reviewed international journals including over 200 papers published on IEEE Transactions journals.

Hao Zhang received the B.Sc. degree in automatic control from Wuhan University of Technology, Wuhan, China, in 2001 and received Ph.D. degree in control theory and control engineering from Huazhong University of Science and Technology Wuhan, China, in 2007. Currently, she is Professor with the School of Electronics and Information Engineering, Tongji University, Shanghai, China. From December 2011 to December 2013. She is also Postdoctoral Fellow with the City University of Hong Kong, Hong Kong. Prof. Zhang was a Changjiang Scholar Distinguished Professor under the Ministry of Education in 2023 and also a recipient of the National Science Fund for Excellent Young Scholars of China in 2019.

Meng Wang received the B.Eng. degree in Automation from Northeastern University at Qinhuangdao, Qinhuangdao, China, in 2011, and the M.Eng. degree in Control Science and Engineering from Harbin Institute of Technology, Harbin, China, in 2013, and the Ph.D. degree from the Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Hong Kong SAR, China, in 2018. He is currently Associate Professor at School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China. His research interests include robust control and filtering, fuzzy systems and control, and their engineering applications.

Zhichen Li received the B.S. degree in automation and the Ph.D. degree in pattern recognition and intelligent systems from North China Electric Power University, Beijing, China, in 2011 and 2017, respectively. From 2017 to 2019, he was Postdoctoral Fellow with the School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China, where he is currently Professor. Prof. Li was a Changjiang Young Scholar under the Ministry of Education in 2024. His research interests include networked control systems, active disturbance rejection control, robots, and fuzzy modeling and control.