文章基本信息

标题：Probabilistic H 2 -norm estimation via Gaussian process system identification ⁎
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作者：Daniel Persson ; Anne Koch ; Frank Allgöwer 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：431-436
DOI：10.1016/j.ifacol.2020.12.211
语种：English
出版社：Elsevier
摘要：AbstractWe present a method for data-based estimation of the H2-norm of a linear time-invariant system from input-output data in a probabilistic setting by employing the recent advances in Gaussian process system identification using stable-spline kernels. Advantages of this starting point include that the norm can be estimated for the continuous-time system and over infinite horizon, even though only a finite number of measurements are available. We approximate theH2-norm distribution as Gaussian, whose expectation can even be obtained analytically, while we use a numerical scheme based on Gaussian process quadrature for the variance. Not only do we utilize the posterior variance of the Gaussian process to derive an error estimate for theH2-norm, but also to tune the estimation by optimizing the input sequence. The performance of the developed scheme is thoroughly evaluated in simulation.
关键词：KeywordsNonparametric methodsBayesian methodsData-based controlIdentification for controlInputexcitation design