An FNN-Based adaptive iterative learning control for a class of nonlinear discrete-time systems

Abstract

In this paper, a fuzzy neural network is applied to design a discrete adaptive iterative learning controller for a class of nonlinear discrete-time systems. The fuzzy neural network is used as a function approximator to compensate the unknown certainty equivalent controller. The problem of function approximation error is solved by a technique of time-varying boundary layer. This boundary layer is then utilized to construct an auxiliary error function for the design of adaptive laws. In order to achieve a desired learning performance, the FNN parameter and the width of boundary layer will be tuned during the iteration processes. Based on a Lyapunov-like analysis, we show that all adjustable parameters as well as the internal signals remain bounded for all iterations and the output tracking error will asymptotically converge to a residual set whose size depends on the width of boundary layer as iteration goes to infinity.