文章基本信息

标题：Dynamic Iterative Principal Components Analysis for Closed-loop, Model Identification
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作者：Richa Katare ; Deepak Maurya ; Ravindra D. Gudi 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：1
页码：393-398
DOI：10.1016/j.ifacol.2022.04.065
语种：English
出版社：Elsevier
摘要：AbstractIdentification of closed-loop systems from the input-output data has been studied quite extensively for several decades. In this work, we extend the use of the dynamic iterative principal components analysis (DIPCA algorithm by Maurya et al.) to the task of identification of the model in closed-loop, where the input and output are corrupted with heteroscedastic white-noise also known as the errors-in-variables class of problems. We develop and evaluate the DIPCA approach for two methods, viz., the direct method and the two-step method for closed-loop system identification. Monte Carlo simulation results are presented to demonstrate the consistency of the results.
关键词：KeywordsClosed-loop IdentificationPrincipal Components AnalysisErrors-in-Variables