Joining consensus of networked multi-agent systems with nonlinear couplings and weighting constraints

Abstract

This paper studies the joining consensus of networked multi-agent systems subject to nonlinear couplings and weighted directed graphs via pinning control. A weighted-average consensus protocol is proposed to achieve the collective decision by interacting with the local information of some pinned agents. By proposing a novel joining consensus protocol, average consensus and general consensus strategies are joined to achieve an agreement for the weighting networked system. Furthermore, by calculating a proper consensus gain and using finite control Lyapunov controllers, an efficient joining consensus protocol is presented to improve the consensus speed. Sufficient conditions for achieving the consensuses asymptotically are proved. Finally, theoretical results are validated via simulations.

Keywords:

• networked multi-agent systems
• average consensus
• joining consensus
• pinning control
• weighting constraints
• finite pinning control Lyapunov functions

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number 61174059], [grant number 61233004]; National 973 Programme of China [grant number 2013CB035406]; Research Project of Shanghai Municipal Economic and Informatization Commission [grant number ZB-ZBYZ-01112634], [grant number 12GA-31).

Notes on contributors

Bohui Wang

Bohui Wang received his BS degree in Institute of Computer Science and Technology from Xian University of Science and Technology, Shaanxi, China, in 2012. He is currently pursuing his PhD degree in the Department of Automation, Shanghai Jiao Tong University, Shanghai, China. Now, his research interests focus on multi-agent systems, nonlinear stochastic systems, machine learning consensus, and networked control systems (NCSs).

Jingcheng Wang

Jingcheng Wang received his BS and MS degrees in Northwestern Polytechnic University, Shaanxi, China, in 1992 and 1995, respectively. He received his PhD degree in Zhejiang University, Zhejiang, China, in 1998. He is a former research fellow of Alexander von Humboldt Foundation in Rostock University, Germany, and is now a professor in Shanghai Jiao Tong University, Shanghai, China. His current research interests include robust control, intelligent control, real-time control, and simulation.

Langwen Zhang

Langwen Zhang received the BS degree in automation from the South China University of Technology, Guangzhou, China, in 2010. He is currently a PhD candidate with the Department of Automation, Shanghai Jiao Tong University, Shanghai, China. His research interests include robust control and model predictive control.

Yang Ge

Yang Ge received the BS degree from the University of Electronic Science and Technology of China, Chengdu, China, and MS degree from Shanghai Jiao Tong University, Shanghai, China, in 2009 and 2011, respectively. He is now a Ph D candidate in Shanghai Jiao Tong University. His research interests include robust control and networked control systems.