Error tracking approach to discrete‐time adaptive iterative learning control against arbitrary initial perturbations. (7th February 2022)

• Record Type: Journal Article
• Authors: Sun, Mingxuan; Zhan, Yizhao; Li, Wei; He, Xiongxiong
• Is Part Of: International journal of adaptive control and signal processing. Volume 36:Number 4(2022)
• Page Start: 943
• Page End: 964
• Publication Date: 2022-02-07
• Subjects: adaptive iterative learning control -- discrete‐time systems -- initial condition problem -- nonidentical tasks -- time‐variant systems; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.3384
• Languages: English
• ISSNs: 0890-6327

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.