文章基本信息

标题：Model Order Selection in Robust-Control-Relevant System Identification
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作者：Paul Tacx ; Robin de Rozario ; Tom Oomen 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：7
页码：1-6
DOI：10.1016/j.ifacol.2021.08.325
语种：English
出版社：Elsevier
摘要：AbstractRobust control allows for guaranteed performance for a range of candidate models. The aim of this paper is to investigate the role of model complexity in the identification of model sets for robust control. A key point is that model quality and model complexity should be evaluated with respect to the control goal. Regularization using a worst-case control criterion in conjunction with a specific model uncertainty structure allows robust control of multivariable systems using accurate models with low complexity. Simulations confirm that the model order should be selected in view of the control objectives. Overall, the framework allows for systematic identification of model sets for robust control.
关键词：KeywordsIdentification for controlRobust controlMotion controlMechatronic systemsFrequency domain identificationIdentificationcontrol methodsOrder selection