This book focuses on distributed event-triggered control of multi-agent systems, in which the event-triggering mechanism is utilized to reduce the communication frequencies in order to compensate for constrained network bandwidths, an aspect that poses significant challenges for distributed control design. The book summarizes the authors' original, systematic contributions on scalability and robustness, two core issues in distributed event-triggered control. Specifically, the book presents fully distributed adaptive event-triggered control laws; as they rely on neither continuous communications nor global information on the network, these laws are scalable with regard to network size and topology. Moreover, the book provides novel and robust event-triggered algorithms, which can accommodate various frequency-domain and matching uncertainties.
The results presented here are applicable to UAV swarms, automobile systems, aerospace systems, vehicle platooning, and more, and will be of considerable interest to graduate students, researchers, and engineers working on network control systems.
Zhongkui Li received his B.S. degree in space engineering from the National University of Defense Technology, China, in 2005, and his Ph.D. in dynamical systems and control from Peking University, China, in 2010. Since 2013, he has been with the Department of Mechanics and Engineering Science, College of Engineering, Peking University, China, where he is currently an Associate Professor. His current research interests include cooperative control and planning of multi-agent systems. Prof. Li was the recipient of the State Natural Science Award of China in 2015, the Natural Science Award of the Ministry of Education of China in 2022 and 2011, and the National Excellent Doctoral Thesis Award of China in 2012. His co-authored papers received the IET Control Theory & Applications Premium Award in 2013 and the Best Paper Award of Journal of Systems Science & Complexity in 2012. He currently serves as an Associate Editor of IEEE Transactions on Automatic Control, Nonlinear Analysis: Hybrid Systems, and an Editor of International Journal of Robust and Nonlinear Control.
Bin Cheng received his B.S. degree in mechanical engineering and automation from the School of Mechanical Engineering, University of Science & Technology Beijing, China, in 2015 and his Ph.D. in dynamical systems and control from the Department of Mechanics and Engineering Science, College of Engineering, Peking University, Beijing, China, in 2020. He is currently an Assistant Professor at the Department of Control Science and Engineering, College of Electronics and Information Engineering, Tongji University, Shanghai, China, and at the Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, China. His current research interests include cooperative control of multi-agent systems, adaptive control, and event-triggered control. He won the Best Student Paper Finalist Award at the IEEE ICCA 2019. He was selected for the Sailing Program (Science and Technology Innovation Action Plan of Shanghai) in 2021.
Weihao Song received his B.S. degree in flight vehicle design and engineering and his Ph.D. degree in aeronautical and astronautical science and technology from the Beijing Institute of Technology, Beijing, China, in 2016 and 2021, respectively. From May 2019 to May 2020, he was a Visiting Scholar with the Department of Computer Science, Brunel University London, London, United Kingdom. He is currently a Postdoctoral Researcher with the College of Engineering, Peking University, Beijing, China. His research interests include Bayesian state estimation, distributed state estimation, nonlinear filtering, and networked control systems. He received the Beijing Excellent Doctoral Dissertation Award in 2022.
Shiqi Zhang received his B.S. degree and Ph.D. in mechanical engineering from the Department of Mechanics and Engineering Science, College of Engineering, Peking University, Beijing, China, in 2017 and 2022, respectively.His current research interests include cooperative control of multi-agent systems, distributed robust and optimal control, distributed optimization and reinforcement learning.
