Complexity reduction for uncertain systems: a projection-based approach

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Abstract

This paper considers the problem of complexity reduction for systems with affine parametric uncertainty. We are interested in the relation between model reduction for a nominal plant and dimension reduction for a parameter vector. By using linear fractional representations of the system, it is shown that a projection-based reduction approach can be applied separately to the generalized plant and the uncertainty block. The error bounds between the original system and its reduced order approximation are derived, and a case study is used to validate our findings.

Original languageEnglish
Title of host publication2016 IEEE 55th Conference on Decision and Control, CDC 2016
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages5769-5774
Number of pages6
ISBN (Electronic)978-1-5090-1837-6
ISBN (Print)978-1-5090-1838-3
DOIs
Publication statusPublished - 27 Dec 2016
Event55th IEEE Conference on Decision and Control (CDC 2016) - Aria Resort and Casino, Las Vegas, United States
Duration: 12 Dec 201614 Dec 2016
Conference number: 55
http://cdc2016.ieeecss.org/

Conference

Conference55th IEEE Conference on Decision and Control (CDC 2016)
Abbreviated titleCDC02016
CountryUnited States
CityLas Vegas
Period12/12/1614/12/16
Internet address

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