Adaptive iterative learning control for discrete-time systems with non-repetitiveness

Abstract

In this study, the problem of adaptive iterative learning control (AILC) for a class of discrete-time nonlinear systems with non-repetitiveness is considered. Two types of non-repetitive problem are studied in the nonlinear system and an AILC method with recursive least squares algorithm is designed to remove the influence of non-repetitiveness. It is shown that, the AILC law can guarantee the asymptotical tracking convergence of system state through rigorous analysis. In the end, an example is given to illustrate the effectiveness of the proposed AILC scheme.