文章基本信息

标题：Data-driven surrogate models for LTI systems via saddle-point dynamics ⁎
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作者：Tim Martin ; 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：953-958
DOI：10.1016/j.ifacol.2020.12.1261
语种：English
出版社：Elsevier
摘要：AbstractFor the analysis, simulation, and controller design of large-scale systems, a surrogate model with small complexity is mostly required. A standard approach to determine such a model is given by modelling the system and applying model-order-reduction techniques. Contrary, we propose a data-driven approach, where the surrogate model of the input-output behaviour of an LTI system is determined from data without modelling the system beforehand. Moreover, we provide a guaranteed bound on the maximal error between the system and the surrogate model in case of noise-free measurements. We analyse the stability and convergence of the presented schemes and apply them on a benchmark system from the model-order-reduction literature.
关键词：Keywordsiterative methodsreduced-order modelsinput-output methodslearning algorithmsgradient methods