Learning control for time-delay systems with iteration-varying uncertainty: a Smith predictor-based approach

Abstract

This study is devoted to the problem of robust iterative learning control (ILC) for time-delay systems (TDS) when the plants are subject to random iteration-varying uncertainties. Using the frequency-domain approach, an ILC scheme is considered within the Smith predictor-based feedback configuration. It shows that if the well-known robust performance condition is satisfied, then an updating law can be obtained directly to guarantee that the ILC process converges in the sense of expectation. In particular, if the unit function is selected as the performance weight, then the expected tracking error converges monotonically to zero as a function of iteration. Two numerical examples are presented to illustrate the effectiveness of the Smith predictor-based ILC.