RBF Neural Network Sliding Mode Consensus of Multiagent Systems with Unknown Dynamical Model of Leader-follower Agents
- 30 Downloads
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.
KeywordsConsensus multiagent RBF neural network nonlinear systems sliding mode
Unable to display preview. Download preview PDF.
- Y. Tang, X. Xing, H. R. Karimi, L. Kocarev, and J. Kurths, “Tracking control of networked multi-agent systems under new characterizations of impulses and its applications in robotic systems,” IEEE Trans. on Industrial Electronronics, vol. 63, no. 2, pp. 1299–1307, 2016. [click]CrossRefGoogle Scholar
- A. Sharaan and Z. E. Fard “State dependent Riccati equation sliding mode observer for mathematical dynamic model of chronic myelogenous leukemia,” International Journal of Engineering Systems Modelling and Simulation, vol. 10, no. sn1.Google Scholar
- A. Sharafian and R. Ghasemi, “Fractional neural observer design for a class of nonlinear fractional chaotic systems,” Neural Computing and Applications, 2017.Google Scholar
- W. He and Y. Dong, “Adaptive fuzzy neural network control for a constrained robot using impedance learning,” IEEE Transactions on Neural Networks and Learning Systems, 2017.Google Scholar
- L. Yu, S. Fei, H. Zu, and X. Li, “Direct adaptive neural control with sliding mode method for a class of uncertain switched nonlinear systems,” International Journal of Innovative Computing, Information and Control, vol. 6, no. 12, pp. 5609–5618, 2010.Google Scholar