Adaptive neural control for a class of uncertain non-affine nonlinear switched systems

This paper presents an indirect adaptive neural network control for a class of uncertain non-affine nonlinear switched systems with an unknown control direction. In this method, the unknown non-affine function is firstly converted into an affine-like form using the mean value theorem. Then, an RBF neural network is utilized to approximate the unknown functions. In addition, Nussbaum-gain technique is employed to deal with the issue of not having a priori knowledge about the gain sign. Furthermore, by adding a robustifying term to the control signal, the proposed approach can handle the approximation error in neural network approximation and external disturbance so that the system robustness can be improved. The proposed controller is designed based on Lyapunov stability theory to ensure the asymptotic stability of the closed-loop system. Finally two numerical simulations are given to show the effectiveness of the proposed scheme.