A Design Scheme of Adaptive Switching Neural Control with Uncertain Nonlinearity and External Disturbance

In this paper, we concern with the robust adaptive tracking control problem using neural networks for switched nonlinear systems with uncertain nonlinearity and external disturbance. The hypothesis condition that the sign of control gain is known has been relaxed by the proposed control strategy. RBF neural networks (NNs) are utilized to model the unknown nonlinear functions and a robust adaptive neural tracking control method is recommended to enhance the switching the system robustness. Based on switched multiple Lyapunov function strategy, we have derived the adaptive updated control law and the appropriate switching law. It is shown that the technique proposed is able to guarantee that the resulting closed-loop system is asymptotically stable in the Lyapunov sense such that the system output tracking error performance can be well obtained. The effectiveness of the presented control method is demonstrated by the simulation results.