Robust point-to-point iterative learning control with trial-varying initial conditions

dc.contributor.authorTao H.
dc.contributor.authorLi J.
dc.contributor.authorChen, Yiyang
dc.contributor.authorStojanović, Vladimir
dc.contributor.authorYang H.
dc.date.accessioned2023-02-08T16:45:05Z
dc.date.available2023-02-08T16:45:05Z
dc.date.issued2020
dc.description.abstractIterative learning control (ILC) is a high-performance technique for repeated control tasks with design postulates on a fixed reference profile and identical initial conditions. However, the tracking performance is only critical at few points in point-topoint tasks, and their initial conditions are usually trial-varying within a certain range in practice, which essentially degrades the performance of conventional ILC algorithms. Therefore, this study reformulates the ILC problem setup for point-to-point tasks and considers the effort of trial-varying initial conditions in algorithm design. To reduce the tracking error, it proposes a worstcase norm-optimal problem and reformulates it into a convex optimisation problem using the Lagrange dual approach. In this sense, a robust ILC algorithm is derived based on iteratively solving this problem. The study also shows that the proposed robust ILC is equivalent to conventional norm-optimal ILC with trial-varying parameters. A numerical simulation case study is conducted to compare the performance of this algorithm with that of other control algorithms while performing a given point-topoint tracking task. The results reveal its efficiency for the specific task and robustness against trial-varying initial conditions.
dc.identifier.doi10.1049/iet-cta.2020.0557
dc.identifier.issn1751-8644
dc.identifier.scopus2-s2.0-85102343186
dc.identifier.urihttps://scidar.kg.ac.rs/handle/123456789/16219
dc.sourceIET Control Theory and Applications
dc.titleRobust point-to-point iterative learning control with trial-varying initial conditions
dc.typearticle

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