Adaptive Neural Network Control of Nonlinear Systems with Input Nonlinearity

The paper investigates the adaptive neural network control design for a class of nonlinear systems with input 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. Furthermore, the adaptive compensation term of the upper bound function of the sum of residual and approximation error is adopted to reduce the effects of modeling error. By Lyapunov method, the tracking error is proved to be asymptotically converging to zero. Simulation results illustrate the effectiveness of the proposed approach