Intelligent robust control for uncertain nonlinear multivariable systems using recurrent cerebellar model neural networks

Abstract

This paper develops an intelligent robust control algorithm for a class of uncertain nonlinear multivariable systems by using a recurrent-cerebellar-modelarticulation- controller (RCMAC) and sliding mode technology. The proposed control algorithm consists of an adaptive RCMAC and a robust controller. The adaptive RCMAC is a main tracking controller utilized to mimic an ideal sliding mode controller, and the parameters of the adaptive RCMAC are on-line tuned by the derived adaptive laws from the Lyapunov function. Based on the H∞ control approach, the robust controller is employed to efficiently suppress the influence of residual approximation error between the ideal sliding mode controller and the adaptive RCMAC, so that the robust tracking performance of the system can be guaranteed. Finally, computer simulation results on a Chua’s chaotic circuit and a three-link robot manipulator are performed to verify the effectiveness and feasibility of the proposed control algorithm. The simulation results confirm that the developed control algorithm not only can guarantee the system stability but also achieve an excellent robust tracking performance.