This paper proposed a new methodology to cover the problem of consensus of multiagent systems with sliding mode control based on Radial Basis Function (RBF) neural network. First, neural network adopted to distinguish the uncertainties of the leader and follower agents then a sliding mode tracking controller is applied to force the follower agents to follow the leader’s time-varying states trajectory with the consensus error as small as possible. As the RBF neural network is adopted to approximate the uncertainties, the results can only achieve local consensus. Different from past literature, total error of consensus protocol is considering for sliding surface therefore the local stability of the whole multiagent system is provided meanwhile RBF neural network overcome the problem of unmodeled leader/follower agent dynamics. The weights of the neural networks updated adaptively directly commensurate with consensus error. The point is, there is absolutely no need to have information about dynamical model of the system. The merits of the proposed approach are consisting of consensus protocol robustness, fast error convergence to zero, and local stability of the closed loop multiagent system which is proved by Lyapunov direct method. The simulation results show promising performance of the proposed method on a chaotic system.
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Recommended by Associate Editor Huanqing Wang under the direction of Editor Myo Taeg Lim. This paper is partially supported by the National Science Foundation of China (61473183, U1509211). The authors would like to thank anonymous reviewers for their constructive suggestions and comments which improve substantially the original manuscript.
Amin Sharafian received his M.Sc. degree in Control Engineering from University of Qom in 2016. He is now a Ph.D. student at school of Automation, Shanghai Jiao Tong University (SJTU). His research interests include nonlinear control, Fuzzy Systems, Neural Networks, Fractional Calculation, Multi-agent Systems and etc.
Vahid Bagheri received the B.S. degree in Electrical Engineering from Shahed University in 2013 and his M.S. degree at University of Qom He is now a Ph.D. scholar at Imam Khomeini International University. His research interests include multiagent system, robust control, and adaptive control.
Weidong Zhang received his BS, MS, and PhD degrees from Zhejiang University, China, in 1990, 1993, and 1996, respectively, and then worked as a Postdoctoral Fellow at Shanghai Jiaotong University. He joined Shanghai Jiaotong University in 1998 as an Associate Professor and has been a Full Professor since 1999. From 2003 to 2004 he worked at the University of Stuttgart, Germany, as an Alexander von Humboldt Fellow. He is a recipient of National Science Fund for Distinguished Young Scholars of China. In 2011 he was appointed the Chair Professor at Shanghai Jiaotong University. He is currently the Director of the Engineering Research Center of Marine Automation, Shanghai Municipal Education Commission and Deputy Dean of the Department of Automation, Shanghai Jiao Tong University His research interests include control theory and its applications in industry and ocean engineering. He is the author of more than 300 refereed papers and 1 book, and holds 32 patents.
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Sharafian, A., Bagheri, V. & Zhang, W. RBF Neural Network Sliding Mode Consensus of Multiagent Systems with Unknown Dynamical Model of Leader-follower Agents. Int. J. Control Autom. Syst. 16, 749–758 (2018). https://doi.org/10.1007/s12555-017-0231-y
- RBF neural network
- nonlinear systems
- sliding mode