Iterative Learning Control for Discrete Distributed Parameter Systems With Randomly Varying Trial Lengths

In this paper, iterative learning control (ILC) is employed in discrete spatial-temporal parabolic distributed parameter systems (DPSs), where the trial lengths vary randomly. A distributed ILC strategy is proposed, in which containing spatial variable, utilizes all past tracking information to improve current performance. Through rigorous theoretical analysis, the convergence of the system output error is proved under mathematical expectation along the iteration axis. Finally, the proposed method is applied to numerical simulation to illustrate its effectiveness.