Complexity reduction for uncertain systems: a projection-based approach

A.A. Bachnas; X. Yan; S. Weiland

doi:10.1109/CDC.2016.7799156

Complexity reduction for uncertain systems: a projection-based approach

A.A. Bachnas, X. Yan, S. Weiland

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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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 language	English
Title of host publication	2016 IEEE 55th Conference on Decision and Control, CDC 2016
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	5769-5774
Number of pages	6
ISBN (Electronic)	978-1-5090-1837-6
ISBN (Print)	978-1-5090-1838-3
DOIs	https://doi.org/10.1109/CDC.2016.7799156
Publication status	Published - 27 Dec 2016
Event	55th IEEE Conference on Decision and Control (CDC 2016) - Aria Resort and Casino, Las Vegas, United States Duration: 12 Dec 2016 → 14 Dec 2016 Conference number: 55 http://cdc2016.ieeecss.org/

Conference

Conference	55th IEEE Conference on Decision and Control (CDC 2016)
Abbreviated title	CDC02016
Country/Territory	United States
City	Las Vegas
Period	12/12/16 → 14/12/16
Internet address	http://cdc2016.ieeecss.org/

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10.1109/CDC.2016.7799156

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title = "Complexity reduction for uncertain systems: a projection-based approach",

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.",

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Bachnas, AA, Yan, X & Weiland, S 2016, Complexity reduction for uncertain systems: a projection-based approach. in 2016 IEEE 55th Conference on Decision and Control, CDC 2016., 7799156, Institute of Electrical and Electronics Engineers, Piscataway, pp. 5769-5774, 55th IEEE Conference on Decision and Control (CDC 2016), Las Vegas, Nevada, United States, 12/12/16. https://doi.org/10.1109/CDC.2016.7799156

Complexity reduction for uncertain systems: a projection-based approach. / Bachnas, A.A.; Yan, X.; Weiland, S.
2016 IEEE 55th Conference on Decision and Control, CDC 2016. Piscataway: Institute of Electrical and Electronics Engineers, 2016. p. 5769-5774 7799156.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AB - 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.

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Y2 - 12 December 2016 through 14 December 2016

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Complexity reduction for uncertain systems: a projection-based approach

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