Equivalent sliding mode controller for ball and plate system based on RBF optimized by PSO

Abstract

This paper presented an equivalent sliding mode control based on radial basis function (RBF) neural network optimized by particle swarm optimization (PSO) for the tracking control of the ball and plate system. Thus the advantage that RBF neural network could approach any function was played, and the robustness of sliding mode control (SMC) was retained. Simulation results show that the method has strong robustness, and tracking error is smaller. © 2012 ICIC International.