Abstract
The paper is concerned with identification of continuous-time systems based on iterative learning control (ILC) in the presence of measurement noise. First, we propose a new ILC method which achieves tracking for uncertain plants by iteration of trials. The distinguished feature of this method is that (i) we do not need any time derivative of I/O signals and (ii) it takes account of noise reduction explicitly by using I/O data of all past trials. Second, it is shown how to estimate parameters of a class of linear continuous-time systems based on the proposed ILC method in noisy circumstances. Its effectiveness is demonstrated through numerical examples.
