文章基本信息

标题：Experiment design for impulse response identification with signal matrix models ⁎
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作者：Andrea Iannelli ; Mingzhou Yin ; Roy S. Smith 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：625-630
DOI：10.1016/j.ifacol.2021.08.430
语种：English
出版社：Elsevier
摘要：AbstractThis paper formulates an input design approach for truncated infinite impulse response identification in the context of implicit model representations recently used as basis for data-driven simulation and control approaches. Precisely, the considered model consists of a linear combination of the columns of a data (or signal) matrix. An optimal combination for the case of noisy data was recently proposed using a maximum likelihood approach, and the objective here is to optimize the input entries of the data matrix such that the mean-square error matrix of the estimate is minimized. A least-norm problem is derived in terms of the optimality criteria typically considered in the experiment design literature. Numerical results showcase the improved estimation fit achieved with the optimized input.
关键词：KeywordsSystem identificationExperiment designData-driven methodsIIR estimation