Structured coprime factor model reduction based on LMIs

Li, L; Paganini, F

Structured coprime factor model reduction based on LMIs

Li, L

Paganini, F

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Publication Type:: Journal Article
Citation:: Automatica, 2005, 41 (1), pp. 145 - 151
Issue Date:: 2005-01-01

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Field	Value	Language
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216	en_US
dc.contributor.author	Paganini, F	en_US
dc.date.issued	2005-01-01	en_US
dc.identifier.citation	Automatica, 2005, 41 (1), pp. 145 - 151	en_US
dc.identifier.issn	0005-1098	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15280
dc.description.abstract	In this paper we discuss dynamic model reduction methods which preserve a certain structure in the underlying system. Specifically, we consider the situation where the reduction must be consistent with a partition of the system states. This is motivated, for instance, in situations where state variables are associated with the topology of a networked system, and the reduction should preserve this. We build on the observation that imposing block structure to generalized controllability and observability gramians automatically yields such state-partitioned model reduction. The difficulty lies in ensuring feasibility of the resulting Lyapunov inequalities, which is in general very restrictive. To overcome this, we consider coprime factor model reduction. We derive an LMI characterization of expansive and contractive coprime factorizations that preserve structure, and use this to build a more flexible method for structured model reduction. An example is given to illustrate the method. © 2004 Elsevier Ltd. All rights reserved.	en_US
dc.relation.ispartof	Automatica	en_US
dc.relation.isbasedon	10.1016/j.automatica.2004.09.003	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Structured coprime factor model reduction based on LMIs	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	41	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	41	en_US

Abstract:

In this paper we discuss dynamic model reduction methods which preserve a certain structure in the underlying system. Specifically, we consider the situation where the reduction must be consistent with a partition of the system states. This is motivated, for instance, in situations where state variables are associated with the topology of a networked system, and the reduction should preserve this. We build on the observation that imposing block structure to generalized controllability and observability gramians automatically yields such state-partitioned model reduction. The difficulty lies in ensuring feasibility of the resulting Lyapunov inequalities, which is in general very restrictive. To overcome this, we consider coprime factor model reduction. We derive an LMI characterization of expansive and contractive coprime factorizations that preserve structure, and use this to build a more flexible method for structured model reduction. An example is given to illustrate the method. © 2004 Elsevier Ltd. All rights reserved.

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http://hdl.handle.net/10453/15280