Consensus Over Switching Network Topology: Characterizing System Parameters and Joint Connectivity - Qin, Jiahu;Ma, Qichao;Gao, Huijun
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This book aims to extend existing works on consensus of multi-agent systems systematically. The agents to be considered range from double integrators to generic linear systems. The primary goal is to explicitly characterize how agent parameters, which reflect both self-dynamics and inner coupling of each agent, and switching network topologies jointly influence the collective behaviors. A series of necessary and/or sufficient conditions for exponential consensus are derived. The contents of this book are as follows. Chapter 1 provides the background and briefly reviews the advances of…mehr

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Produktbeschreibung
This book aims to extend existing works on consensus of multi-agent systems systematically. The agents to be considered range from double integrators to generic linear systems. The primary goal is to explicitly characterize how agent parameters, which reflect both self-dynamics and inner coupling of each agent, and switching network topologies jointly influence the collective behaviors. A series of necessary and/or sufficient conditions for exponential consensus are derived.
The contents of this book are as follows. Chapter 1 provides the background and briefly reviews the advances of consensus of multi-agent systems. Chapter 2 addresses the consensus problem of double integrators over directed switching network topologies. It is proven that exponential consensus can be secured under very mild conditions incorporating the damping gain and network topology. Chapter 3 considers generic linear systems with undirected switching network topologies. Necessary and sufficient conditions on agent parameters and connectivity of the communication graph for exponential consensus are provided. Chapter 4 furthers the study of consensus for multiple generic linear systems by considering directed switching network topologies. How agent parameters and joint connectivity work together for reaching consensus is characterized from an algebraic and geometric view. Chapter 5 extends the design and analysis methodology to containment control problem, where there exist multiple leaders. A novel analysis framework from the perspective of state transition matrix is developed. This framework relates containment to consensus and overcomes the difficulty of construction of a containment error.
This book serves as a reference to the main research issues and results on consensus of multi-agent systems. Some prerequisites for reading this book include linear system theory, matrix theory, mathematics, and so on.
Autorenporträt
Jiahu Qin (Senior Member, IEEE) received his first Ph.D. degree in control science and engineering from Harbin Institute of Technology in 2012, and the second Ph.D. degree in systems and control from The Australian National University, Australia, in 2014. He is currently a Professor at the Department of Automation, University of Science and Technology of China. His current research interests include autonomous intelligent systems, cyber-physical systems, and human-robot interaction. Yanni Wan received the B.E. degree in automation from Ocean University of China, Qingdao, China, in 2016, and the Ph.D. degree in control science and engineering from University of Science and Technology of China, Hefei, China, in 2022. She is currently a Lecturer at the School of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan, China. Her research interests include distributed energy management in smart grids and charging/discharging scheduling of EVs. Fangyuan Li(Member, IEEE) received the B.E. degree in electrical engineering and its automation from Southwest Jiaotong University, Chengdu, China, in 2014, and the Ph.D. degree in control science and engineering from University of Science and Technology of China, Hefei, China, in 2019. He is currently an Associate Professor with the School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, China. His research interests include distributed optimization and control in multi-agent systems and distributed energy management in smart grids. Yu Kang (Senior Member, IEEE) received the Ph.D. degree in control theory and control engineering from University of Science and Technology of China, Hefei, China, in 2005. He is currently a Professor with the Department of Automation and the Institute of Advanced Technology, University of Science and Technology of China. His current research interests include monitoring of vehicle emissions, adaptive/robust control, variable structure control, mobile manipulator, and Markovian jump systems. Weiming Fu (Member, IEEE) received the B.E. degree in automation and the Ph.D. degree in control science and engineering from University of Science and Technology of China, Hefei, China, in 2014 and 2020, respectively. He is currently an Associate Professor with the Department of Automation, University of Science and Technology of China. His research interests include consensus in multi-agent systems and security in cyber-physical systems.