Sliding mode control based on RBF neural networks

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.