Abstract
This paper investigates the cluster consensus problems of generic linear multi-agent systems with switching topologies. Sufficient criteria for cluster consensus, which generalise the results in existing literatures, are derived for both state feedback and observer-based control schemes. By using an averaging method, it is shown that cluster consensus can be achieved when the union of the acyclic topologies contains a directed spanning tree within each cluster frequently enough. We also provide a principle to construct digraphs with inter-cluster cyclic couplings that promote cluster consensus regardless of the magnitude of inter-agent coupling weights. Finally, numerical examples are given to demonstrate the effectiveness of the proposed approaches.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
Additional information
Funding
Notes on contributors
Bo Hou
Bo Hou received his BS and MS degrees from the High-tech Institute of Xi'an in 2009 and 2012. He is now a joint training PhD student of the Department of Computer Science and Technology in Tsinghua University and the Department of Automation in High-tech Institute of Xi'an. His research interests include multi-agent coordination and satellite navigation signal simulation.
Fuchun Sun
Hongbo Li
Yao Chen
Yao Chen received his BS degree in mathematics from the Three Gorges University, China, in 2007, and his PhD degree from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China, in 2012. Currently, he is a research assistant in the Department of Computing, Hong Kong Polytechnic University. His research interests include complex networks, multi-agent systems, rail traffic control and optimization.
Jianxiang Xi
Jianxiang Xi received his BS, MS and PhD degrees from the High-tech Institute of Xi'an, China, in 2004, 2007 and 2012, respectively. He is currently an associate professor in the High-tech Institute of Xi'an. His research interests include complex systems control, switched systems and multi-agent systems.