Adaptive-neural control of a class of unknown nonlinear discrete-time systems

In this paper, an adaptive controller based on neural networks is derived for controlling a class of unknown nonlinear discrete-time systems. The learning algorithm, Widrow-Hoff delta rule, is used to minimize the error signal. It is proved that the control objective is achieved by the closed-loop system and that the system remains closed-loop stability. The effectiveness of the proposed control scheme is also demonstrated by a simulation example.