Adaptive neural network control of nonlinear systems with unknown dead-zone model

Abstract

The problem of direct adaptive neural network control for a class of uncertain nonlinear systems with unknown dead-zone model and unknown constant control gain is studied in this paper. Based on simplified dead-zone model and the supervisory control strategy as well as the approximation capability of multilayer neural networks (MNNs), a novel design scheme of direct adaptive integral variable structure neural network controller is proposed. The adaptive law of the adjustable parameter vector and the matrix of weights in the neural networks and the gain of sliding mode control term to adaptively compensate for the residual and the approximation error of MNNs is determined by using a Lyapunov method. The approach does not require the optimal approximation error being square-integrable or the supremum of the optimal approximation error to be known. By theoretical analysis, the closed-loop control system is proven to be globally stable in the sense that all signals involved are bounded, with tracking error converging to zero.