Second-Level Adaptation and Optimization for Multiple Model Adaptive Iterative Learning Control

Abstract

In this paper, we present a two-tier approach to achieve faster convergence in the presence of parameter uncertainties for discrete-time Iterative Learning Control (ILC) systems. The Multiple-Models with Second-Level Adaptation (MM-SLA) methodology is presented to minimize the time taken for tracking error to converge. The advantages of such an approach have not been exploited thus far in the context of adaptive ILC (AILC). We show here that AILC with MM-SLA leads to a significant reduction in the control energy besides faster convergence in the tracking error. Using simulation examples, we demonstrate the efficacy of the proposed strategies.