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Adaptive sliding mode control of dynamic system using RBF neural network

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Abstract

This paper presents a robust adaptive sliding mode control strategy using radial basis function (RBF) neural network (NN) for a class of time varying system in the presence of model uncertainties and external disturbance. Adaptive RBF neural network controller that can learn the unknown upper bound of model uncertainties and external disturbances is incorporated into the adaptive sliding mode control system in the same Lyapunov framework. The proposed adaptive sliding mode controller can on line update the estimates of system dynamics. The asymptotical stability of the closed-loop system, the convergence of the neural network weight-updating process, and the boundedness of the neural network weight estimation errors can be strictly guaranteed. Numerical simulation for a MEMS triaxial angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive RBF sliding mode control scheme.

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Acknowledgements

This work is partially supported by National Science Foundation of China under Grant No. 61074056, the Natural Science Foundation of Jiangsu Province under Grant No. BK2010201, and the Fundamental Research Funds for the Central Universities under Grant No. 2012B06714.

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  1. Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, College of Computer and Information, Hohai University, Changzhou, 213022, China

    Juntao Fei & Hongfei Ding

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  1. Juntao Fei
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  2. Hongfei Ding
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Correspondence to Juntao Fei.

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Fei, J., Ding, H. Adaptive sliding mode control of dynamic system using RBF neural network. Nonlinear Dyn 70, 1563–1573 (2012). https://doi.org/10.1007/s11071-012-0556-2

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