Sliding mode control for uncertain nonlinear systems using RBF neural networks

A robust sliding mode adaptive tracking controller using RBF neural networks is proposed for uncertain SISO nonlinear dynamical systems with unknown nonlinearity. The Lyapunov synthesis approach and sliding mode method are used to develop a state-feedback adaptive control algorithm by using RBF neural networks. Furthermore, the H_∞, tracking design technique and the sliding mode control method are incorporated into the adaptive neural networks control scheme so that the derived controller is robust with respect to disturbances and approximate errors. Compared with conventional methods, the proposed approach assures closed-loop stability and guarantees an H_∞ tracking performance for the overall system. Simulation results verify the effectiveness of the designed scheme and the theoretical discussions.