DIRECT ADAPTIVE NEURAL CONTROL WITH SLIDING MODE METHOD FOR A CLASS OF UNCERTAIN SWITCHED NONLINEAR SYSTEMS

In this paper, a direct adaptive neural control scheme with sliding mode method is presented for a class of switched nonlinear systems which is in the presence of model uncertainties and external disturbances. A sliding mode controller based on RBF neural networks (RBF NNs) which are utilized to approximate unknown functions and uncertain nonlinear terms is designed to enhance robustness and maintain boundedness. Adaptive neural updated laws are based on switched Lyapunov function approach, and a periodically switching signal is constructed. It is proved that with the proposed control scheme, the resulting closed-loop switched system is robustly stable and uniformly ultimately bounded such that the satisfactory tracking performance and sliding surface are both well achieved. A simulation example is provided to illustrate the effectiveness and the feasibility of the proposed approach.