Open-Closed-Loop PD-Type Iterative Learning Control with Variable Gains

Abstract

In this paper an open-closed-loop PD-type Iterative Learning Control (ILC) algorithm with variable learning gains is proposed. The learning gains are varying with the system errors or the iteration times. Thus it can eliminate the errors fast and reduce the overshoot. Therefore the capability of the target tracking is greatly improved. A rigorous proof of the sufficient condition for the algorithm is given, which shows the convergence of the tracking error.