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Data-driven model reduction by moment matching for linear and nonlinear systems

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Title: Data-driven model reduction by moment matching for linear and nonlinear systems
Authors: Scarciotti, G
Astolfi, A
Item Type: Journal Article
Abstract: Theory and methods to obtain reduced order models by moment matching from input/output data are presented. Algorithms for the estimation of the moments of linear and nonlinear systems are proposed. The estimates are exploited to construct families of reduced order models. These models asymptotically match the moments of the unknown system to be reduced. Conditions to enforce additional properties, e.g. matching with prescribed eigenvalues, upon the reduced order model are provided and discussed. The computational complexity of the algorithms is analyzed and their use is illustrated by two examples: we compute converging reduced order models for a linear system describing the model of a building and we provide, exploiting an approximation of the moment, a nonlinear planar reduced order model for a nonlinear DC-to-DC converter.
Issue Date: 1-May-2017
Date of Acceptance: 6-Jan-2017
URI: http://hdl.handle.net/10044/1/43816
DOI: 10.1016/j.automatica.2017.01.014
ISSN: 0005-1098
Publisher: Elsevier
Start Page: 340
End Page: 351
Journal / Book Title: Automatica
Volume: 79
Issue: 1
Copyright Statement: © 2017 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/L014343/1
Keywords: Science & Technology
Technology
Automation & Control Systems
Engineering, Electrical & Electronic
Engineering
Model reduction
System identification
Model reduction from data
Moment matching
TIME-DELAY SYSTEMS
BALANCED REALIZATION
APPROXIMATIONS
INTERPOLATION
IDENTIFICATION
OBSERVABILITY
FAMILIES
Industrial Engineering & Automation
01 Mathematical Sciences
08 Information and Computing Sciences
09 Engineering
Publication Status: Published
Online Publication Date: 2017-03-06
Appears in Collections:Electrical and Electronic Engineering
Faculty of Engineering