Neural-networks-based distributed output regulation of multi-agent systems with nonlinear dynamics

Authors:
Jia Liu;Zengqiang Chen;Xinghui Zhang;Zhongxin Liu
Affiliations:
Department of Automation, Nankai University, Tianjin 300071, China and Tianjin University of Technology and Education, Tianjin 300222, China;Department of Automation, Nankai University, Tianjin 300071, China;Tianjin University of Technology and Education, Tianjin 300222, China;Department of Automation, Nankai University, Tianjin 300071, China
Venue:
Neurocomputing
Year:
2014

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Abstract

This paper deals with the output regulation problem of the nonlinear multi-agent systems based on dynamic neural networks. Assume that the models of following agents in the considered systems are unknown, and the state of the leader agent is not completely measurable for each follower. By employing Lyapunov approach, a dynamic neural network is established to approximate the systems of the following agents. Based on the dynamic neural network, a state feedback control law is designed guaranteeing the following agents can asymptotically track the reference generated by an exosystem. The exosystem is regarded as the active leaders in the multi-agent systems. A numerical simulation example is provided to demonstrate the effectiveness of the obtained results.