Data-driven model order reduction simultaneously matching linear and nonlinear moments
File(s)
Author(s)
Mao, Junyu
Scarciotti, Giordano
Type
Journal Article
Abstract
In this letter, we address a model reduction problem in which the resulting reduced-order model simultaneously matches sets of linear and nonlinear moments. We propose a framework for approximating the reduced-order models from time-domain input-output data without requiring knowledge of the state-space representation of the system. The developed theory and the proposed data-driven procedure are demonstrated on a benchmark model showing matching of signals generated by a linear filter and a (nonlinear) Van der Pol oscillator, simultaneously.
Date Issued
2024-10-04
Date Acceptance
2024-05-25
Citation
IEEE Control Systems Letters, 2024, 8, pp.2331-2336
ISSN
2475-1456
Publisher
Institute of Electrical and Electronics Engineers
Start Page
2331
End Page
2336
Journal / Book Title
IEEE Control Systems Letters
Volume
8
Copyright Statement
Copyright © 2024 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Identifier
https://ieeexplore.ieee.org/document/10547220
Publication Status
Published
Date Publish Online
2024-06-03