Cooperative Control of Multi-Agent Systems

Distributed controller design is generally a challenging task, especially for multi-agent systems with complex dynamics, due to the interconnected effect of the agent dynamics, the interaction graph among agents, and the cooperative control laws. Cooperative Control of Multi-Agent Systems: A Consensus Region Approach offers a systematic framework for designing distributed controllers for multi-agent systems with general linear agent dynamics, linear agent dynamics with uncertainties, and Lipschitz nonlinear agent dynamics. Beginning with an introduction to cooperative control and graph theory, this monograph: - Explores the consensus control problem for continuous-time and discrete-time linear multi-agent systems - Studies the H8 and H2 consensus problems for linear multi-agent systems subject to external disturbances - Designs distributed adaptive consensus protocols for continuous-time linear multi-agent systems - Considers the distributed tracking control problem for linear multi-agent systems with a leader of nonzero control input - Examines the distributed containment control problem for the case with multiple leaders - Covers the robust cooperative control problem for multi-agent systems with linear nominal agent dynamics subject to heterogeneous matching uncertainties - Discusses the global consensus problem for Lipschitz nonlinear multi-agent systems Cooperative Control of Multi-Agent Systems: A Consensus Region Approach provides a novel approach to designing distributed cooperative protocols for multi-agent systems with complex dynamics. The proposed consensus region decouples the design of the feedback gain matrices of the cooperative protocols from the communication graph and serves as a measure for the robustness of the protocols to variations of the communication graph. By exploiting the decoupling feature, adaptive cooperative protocols are presented that can be designed and implemented in a fully distributed fashion.

Zhongkui Li holds a BS from the National University of Defense Technology, Changsha, China and a Ph.D from Peking University, Beijing, China. He is currently an assistant professor in the Department of Mechanics and Engineering Science, College of Engineering, Peking University, China. Previously he was a postdoctoral research associate at the Beijing Institute of Technology, and held visiting positions at City University of Hong Kong, China and Nanyang Technological University, Singapore. He was the recipient of the Natural Science Award (First Prize) from the Ministry of Education of China in 2011 and the National Excellent Doctoral Thesis Award of China in 2012. His article (coauthored with Z.S. Duan and G.R. Chen) received the 2013 IET Control Theory & Applications Premium Award (Best Paper). Zhisheng Duan holds an MS from Inner Mongolia University, Hohhot, China, and a Ph.D from Peking University, Beijing, China. He is currently a Cheung Kong scholar at Peking University, and is with the Department of Mechanics and Engineering Science, College of Engineering. Previously he was a postdoctor with Peking University; a visiting professor with Monash University, Melbourne, Australia; and a visiting professor with City University of Hong Kong, China. He has been the recipient of the Chinese Control Conference Guan Zhao-Zhi Award and the Natural Science Award (First Prize) from the Ministry of Education of China. He obtained the outstanding National Natural Science Foundation in China, and was selected into the Program for New Century Excellent Talents in Universities by the Ministry of Education of China. He has published over 100 papers in, and been an associate editor and advisory board member of, numerous international referred journals and conferences.

Introduction and Mathematical Background. Consensus Control of Linear Multi-Agent Systems: Continuous-Time Case. Consensus Control of Linear Multi-Agent Systems: Discrete-Time Case. H8 and H2 Consensus Control of Linear Multi-Agent Systems. Consensus Control of Linear Multi-Agent Systems Using Distributed Adaptive Protocols. Distributed Tracking of Linear Multi-Agent Systems with a Leader of Possibly Nonzero Input. Containment Control of Linear Multi-Agent Systems with Multiple Leaders. Distributed Robust Cooperative Control for Multi-Agent Systems with Heterogeneous Matching Uncertainties. Global Consensus of Multi-Agent Systems with Lipchitz Non-Linear Dynamics. Bibliography.