An iterative learning control algorithm within prescribed input-output subspace

This paper is concerned with an iterative learning control (ILC) method for linear continuous-time systems. With the iteration of experiments, the ILC method yields the desired input for tracking the target trajectory. Most o f the former ILC methods use the compensations, such as the time derivative of the error signal or the dual mapping of systems in the learning algorit hm. We propose a new ILC algorithm which does not use such compensations co ntrary to the former methods. In this method, we restrict the input space t o the prescribed finite-dimensional subspace, and use the signal sequence w hich is derived from the projection of the error on this input subspace whe n the input is updated. The effectiveness of the proposed method is demonst rated by a numerical example and experimental evaluation is performed using a two-mass spring system. (C) 2001 Elsevier Science Ltd. All rights reserv ed.