An adaptive approach to iterative learning control with experiments on an industrial robot

Abstract

An adaptive approach to Iterative Learning Control (ILC) based on a Kalman filter and an optimization of a quadratic criterion is presented. By estimating one of the design parameters in the Kalman filter an adaptive gain in the ILC updating formula is created. The proposed ILC design is compared with two other ILC schemes and they are all implemented on an industrial robot. The results show that the proposed adaptive ILC scheme is fast, and also robust since the gain is reduced as the error is decreased.