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RBF Neural Network Sliding Mode Consensus of Multiagent Systems with Unknown Dynamical Model of Leader-follower Agents

  • Amin Sharafian
  • Vahid Bagheri
  • Weidong Zhang
Regular Paper Control Theory and Applications

Abstract

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.

Keywords

Consensus multiagent RBF neural network nonlinear systems sliding mode 

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Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of AutomationShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Department of Electrical EngineeringImam Khomeini International UniversityQazvinIran
  3. 3.Department of AutomationShanghai Jiao Tong UniversityShanghaiChina
  4. 4.School of Mechatronic Engineering and AutomationShanghai UniversityShanghaiChina

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