文章基本信息

标题：Nonparametric continuous-time identification of linear systems: theory, implementation and experimental results
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作者：M. Mazzoleni ; M. Scandella ; S. Formentin 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：20
页码：699-704
DOI：10.1016/j.ifacol.2021.11.253
语种：English
出版社：Elsevier
摘要：AbstractThis paper presents an algorithm for continuous-time identification of linear dynamical systems using kernel methods. When the system is asymptotically stable, also the identified model is guaranteed to share such a property. The approach embeds the selection of the model complexity through optimization of the marginal likelihood of the data thanks to its Bayesian interpretation. The output of the algorithm is the continuous-time transfer function of the estimated model. In this work, we show the algorithmic and computational details of the approach, and test it on real experimental data from an Electro Hydro-Static Actuator (EHSA).
关键词：KeywordsSoftware for system identificationKernel methods