Optimal input filtering for networked iterative learning control systems with packet dropouts and channel noises in both sides

Abstract

Abstract This article is concerned with the convergence performance of wireless networked iterative learning control (ILC) systems with data dropouts and channel noises in both sensor‐to‐controller and controller‐to‐actuator channels. In order to improve the convergence performance of such ILC systems, an optimal input filter is developed at the actuator side to estimate controller updated input with the effect of these network uncertainties. Specifically, a filtering model is constructed only by using the learning process of P‐type controllers and the transmission process of both measured output data and updated input data. On the basis of this model and the orthogonality principle, the filter in the sense of linear minimum variance is designed at the actuator side so that the controller updated input could be estimated in iteration domain. The convergence performance of filtering error covariance matrix is analyzed theoretically. Moreover, because the filter design does not employ plant information, the tracking performance of any plant with P‐type learning controllers can be improved by driving with the filtered input. Simulation results show that the proposed filtering method is effective.