文章基本信息

标题：Structured vector fitting framework for mechanical systems
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作者：Steffen W.R. Werner ; Ion Victor Gosea ; Serkan Gugercin 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：20
页码：163-168
DOI：10.1016/j.ifacol.2022.09.089
语种：English
出版社：Elsevier
摘要：AbstractWe develop a structure-preserving formulation of the data-driven vector fitting algorithm for modally damped mechanical systems. Using the structured pole-residue form of the transfer function of modally damped second-order systems, we propose two potential, structured extensions of the barycentric formula for system transfer functions. Integrating these new forms within the classical vector fitting algorithm leads to the formulation of two new algorithms. These allow the computation of modally damped mechanical systems from data in a least-squares fashion, where the learned model is guaranteed to have the desired structure. We test the proposed algorithms on two benchmark models.
关键词：Keywordsdata-driven modelingmechanical systemsreduced-order modelingvector fittingleast-squares fitbarycentric forms