Analysis and Robust Optimal Design of Iteration Learning Control

Abstract

In this paper we address two most important problems in the field of Iterative Learning Control (ILC): how to quantify and analyze the system performance in iteration domain, and how to design robust optimal ILC according to performance indices specified in iteration domain and in the presence of system interval uncertainties. Two new performance indices - convergence speed in terms of R-factor and global uniform bound are introduced in iteration domain with detailed analysis, which not only provide rigorous assessment for ILC performance, but also facilitate the ILC optimal design with quantified criteria. A robust optimization design with min-max technique is developed, which takes into consideration of the two performance indices and the worst case interval uncertainties simultaneously. Through rigorous analysis, it shows that the proposed design can optimize the two performance indices consistently. For illustration purpose two examples are provided.