Error tracking approach to discrete‐time adaptive iterative learning control against arbitrary initial perturbations

Abstract

Summary Initial condition problem is crucial to a conventional iterative learning control (ILC) scheme, which steers the tracking error from arbitrary initial value to zero in the time steps equaling to the relative degree of the system undertaken. The implementation may be difficult, due to the practical limitation for the control amplitude. This paper presents an error‐tracking approach to discrete‐time adaptive ILC designs for tracking nonidentical tasks in the presence of initial repositioning errors, which may be quite large. A prototype iterative learning algorithm is derived for estimating the time‐varying unknowns, and the saturation is introduced for assuring the estimates to keep away from zero. A key technical lemma, tailored for the analysis purpose in the iteration domain, is presented and applied to analysis of the ILC scheme. The tracking performance of the closed‐loop system is evaluated and explored in detail. It is shown that the perfect tracking for the error between the tracking error and the desired error is achieved at every time instant, while the input and output signals remain bounded. By the simulation example, the proposed scheme is verified to be applicable to tracking tasks without restriction on initial repositioning.

Iterative learning identification: Dynamic parametrization modeling and comparison

“…We refer to literature 26,27 for design techniques and analysis tools, and the references therein. Effort on discrete-time ILC was made for linear time-varying systems described by the input-output representation [28][29][30] and for systems under linear growth conditions. [31][32][33] Recently, there are increasing interests in such learning control methods.…”