文章基本信息

标题：A Finite Time Convergent Least-Squares Modification of the Dynamic Regressor Extension and Mixing Algorithm
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作者：Marijan Palmisano ; Markus Reichhartinger
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：5105-5110
DOI：10.1016/j.ifacol.2020.12.1144
语种：English
出版社：Elsevier
摘要：AbstractThe recently proposed Dynamic Regressor Extension and Mixing (DREM) algorithm can be used to estimate the parameters of structured uncertainties contained in the mathematical model of a plant. In order to provide an adaptation that is less sensitive to the unavoidable mismatch between a plant and its model a least-squares based modification of the DREM estimator is proposed in this paper. The modified estimator yields significantly better estimation results as illustrated by the conducted real-world experiment and its parameter estimates also converge within finite time.
关键词：KeywordsNonlinear observersfilter designparameter identificationfinite time estimationparameter estimationnonlinear regressor