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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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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.

Originele taal-2	Engels
Titel	2016 IEEE 55th Conference on Decision and Control, CDC 2016
Plaats van productie	Piscataway
Uitgeverij	Institute of Electrical and Electronics Engineers
Pagina's	5769-5774
Aantal pagina's	6
ISBN van elektronische versie	978-1-5090-1837-6
ISBN van geprinte versie	978-1-5090-1838-3
DOI's	https://doi.org/10.1109/CDC.2016.7799156
Status	Gepubliceerd - 27 dec. 2016
Evenement	2016 IEEE 55th Conference on Decision and Control (CDC) - Aria Resort and Casino, Las Vegas, Verenigde Staten van Amerika Duur: 12 dec. 2016 → 14 dec. 2016 Congresnummer: 55 http://cdc2016.ieeecss.org/

Congres

Congres	2016 IEEE 55th Conference on Decision and Control (CDC)
Verkorte titel	CDC02016
Land/Regio	Verenigde Staten van Amerika
Stad	Las Vegas
Periode	12/12/16 → 14/12/16
Internet adres	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.",

author = "A.A. Bachnas and X. Yan and S. Weiland",

year = "2016",

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doi = "10.1109/CDC.2016.7799156",

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booktitle = "2016 IEEE 55th Conference on Decision and Control, CDC 2016",

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note = "55th IEEE Conference on Decision and Control (CDC 2016), CDC02016 ; Conference date: 12-12-2016 Through 14-12-2016",

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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, blz. 5769-5774, 2016 IEEE 55th Conference on Decision and Control (CDC), Las Vegas, Nevada, Verenigde Staten van Amerika, 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. blz. 5769-5774 7799156.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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N2 - 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.

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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Complexity reduction for uncertain systems: a projection-based approach

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