An iterative learning control algorithm within prescribed input-output subspace

Abstract

Abstract This paper is concerned with an Iterative Learning Control (ILC) method. With the iteration of experiments, the ILC method yields the desired input for tracking the target trajectory. Most of former ILC methods use the compensations, for instance, the time derivative of the error signal or the dual mapping of systems. We propose a new ILC algorithm which does not use such compensations contrary to the former methods. In this method, we restrict the input space to the prescribed finite-dimensional subspace, and use the signal sequence which is derived from the projection of the error on this input subspace when the input is renewed. The effectiveness of the proposed method is demonstrated by a numerical example and an experiment.