Adaptive Iterative Learning Control of Nonlinear Time-delay Systems Using Neural Network

Abstract

In this paper, an adaptive iterative learning control scheme is presented for a class of strict-feedback nonlinear time-delay systems. The neural network is introduced into iterative learning control. Unknown smooth function vectors and unknown time-delay functions are approximated by two neural networks, respectively. The requirement of traditional iterative learning control algorithm on the nonlinear functions (such as global Lipschitz condition) is relaxed. Furthermore, by using appropriate Lyapunov-Krasovskii functional, all signals in the closed loop system are guaranteed to be semiglobally uniformly ultimately bounded and the output of the system is proved to converge to a small neighborhood of the desired trajectory.