Robust point-to-point iterative learning control with trial-varying initial conditions
dc.contributor.author | Tao H. | |
dc.contributor.author | Li J. | |
dc.contributor.author | Chen, Yiyang | |
dc.contributor.author | Stojanović, Vladimir | |
dc.contributor.author | Yang H. | |
dc.date.accessioned | 2023-02-08T16:45:05Z | |
dc.date.available | 2023-02-08T16:45:05Z | |
dc.date.issued | 2020 | |
dc.description.abstract | Iterative 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.doi | 10.1049/iet-cta.2020.0557 | |
dc.identifier.issn | 1751-8644 | |
dc.identifier.scopus | 2-s2.0-85102343186 | |
dc.identifier.uri | https://scidar.kg.ac.rs/handle/123456789/16219 | |
dc.source | IET Control Theory and Applications | |
dc.title | Robust point-to-point iterative learning control with trial-varying initial conditions | |
dc.type | article |
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