Adaptive Neural Network Control for a Class of Nonlinear Systems with Input Dead-zone Nonlinearity

The paper investigates the adaptive neural network control design for a class of nonlinear systems with input dead-zone nonlinearity using Lyapunov´s stability theory. Based on the principle of sliding mode control and the approximation capability of multilayer neural networks (MNNs), a novel sliding mode neural network control strategy with supervisory controller is developed. With the help of a supervisory controller, the resulting closed-loop system is globally stable in the sense that all signals involved are uniformly bounded. By Lyapunov method, the tracking error is proved to be asymptotically converging to zero.