文章基本信息

标题：A Youla-Kucera Parametrization for Data-Driven Controllers Tuning ⁎
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作者：Freddy Valderrama ; Fredy Ruiz ; Antonio Vicino 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：3989-3994
DOI：10.1016/j.ifacol.2020.12.2262
语种：English
出版社：Elsevier
摘要：AbstractThe Youla-Kucera parametrization is a fundamental result in system theory, very useful when designing model-based controllers. In this paper, this parametrization is employed to solve the controller design from data problem, without requiring a process model. It is shown that employing the proposed controller structure it is possible to achieve more stringent closed-loop performances than previous works in literature, maintaining a criterion to estimate the closed-loop stability. The developed design methodology does not imply a plant identification step and the solution can be obtained by least-squares algorithms in the case of stochastic additive noise. The designed solution is evaluated through Monte Carlo simulations for the regulation problem of an under-damped system.
关键词：KeywordsData-based controlIdentification for controlUncertain systemsParametric optimization