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标题：Supervised Output Regulation via Iterative Learning Control for Rejecting Unknown Periodic Disturbances ⁎
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作者：O. Koçan ; D. Astolfi ; C. Poussot-Vassal 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：1427-1432
DOI：10.1016/j.ifacol.2020.12.1913
语种：English
出版社：Elsevier
摘要：AbstractThe internal model principle (IMC) in linear robust output regulation theory states that a dynamical controller needs to incorporate a copy of the model generating the periodic signals in order to achieve perfect rejection/tracking, robustly with respect to plant’s parameters. On the other hand Iterative Learning Control (ILC) is a data-based approach which not requires anya prioriknowledge, and can be used to find the required control action for attenuating periodic disturbances or tracking periodic references. The control signal generated by ILC includes the frequency and amplitude information of the disturbance and can be used to build the internal model needed for a linear output regulator problem. The objective of this work is therefore that of trying to combine the two approaches, that is IMC and ILC, in order to retain the advantages of each methodology. The proposed methodology, denoted as Supervised Output Regulation via Iterative Learning Control (SOR-ILC), allows to address the problem of output regulation in presence of unknown frequencies. The performances of SOR-ILC are validated through numerical simulations in case of complex periodic disturbances and parameter uncertainties.
关键词：KeywordsIterative learning controlrobust output regulationrejectionperiodic disturbances