Sliding mode control based on RBF neural networks

Ya Zhou; Wu Wang; Xiao-bo Jiao

doi:10.1117/12.2013672

13 March 2013 Sliding mode control based on RBF neural networks

Ya Zhou, Wu Wang, Xiao-bo Jiao

Proceedings Volume 8783, Fifth International Conference on Machine Vision (ICMV 2012): Computer Vision, Image Analysis and Processing; 87830I (2013) https://doi.org/10.1117/12.2013672
Event: Fifth International Conference on Machine Vision (ICMV 12), 2012, Wuhan, China

Abstract

Sliding mode control (SMC) is a special nonlinear control method which have quick response, insensitive to parameters variation and disturbance, online identification for plants are not needed, its very suitable for nonlinear system control, but in reality usage, the chattering reduction and elimination is key problem in SMC. A sliding mode controller based on RBF neural network was designed based on RBF networks’ advantages and learning algorithm, the sliding mode controller was realized with adaptive law and the stability of the proposed control scheme is proved by Lyapnouv theorem. Simulation studies show the methods are effective and can applied into linear or nonlinear control system.

Citation Download Citation

Ya Zhou, Wu Wang, and Xiao-bo Jiao "Sliding mode control based on RBF neural networks", Proc. SPIE 8783, Fifth International Conference on Machine Vision (ICMV 2012): Computer Vision, Image Analysis and Processing, 87830I (13 March 2013); https://doi.org/10.1117/12.2013672

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