文章基本信息

标题：Data-Driven Input-Passivity Estimation Using Power Iterations
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作者：Matias I. Müller ; Anne Koch ; Frank Allgöwer 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：619-624
DOI：10.1016/j.ifacol.2021.08.429
语种：English
出版社：Elsevier
摘要：AbstractIn this work we develop a non-parametric method to estimate the input-passivity index of an unknown linear and time-invariant (LTI) system from iterative experiments based on the power method from numerical linear algebra. Inspired by the power method for estimating the H∞-norm (or L2-gain) from data, we propose an algorithm that time-reverses input-output data in order to emulate measurements of a virtual system whose L2-gain matches the passivity index of the original system under study. While the proposed method requires exciting the original system twice, we also introduce an improved sampling scheme where only one experiment per iteration is needed.
关键词：KeywordsExperiment designnonparametric methodsidentification for control data-driven learningestimation algorithms