LPV state-space identification via IO methods and efficient model order reduction in comparison with subspace methods

E. Schulz, P.B. Cox, R. Toth, H. Werner

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Samenvatting

In this paper, we introduce a procedure for global identification of linear parameter-varying (LPV) discrete-time state-space (SS) models with a static, affine dependency structure in a computationally efficient way. The aim is to develop off-the-shelf LPV-SS estimation methods to make identification practically accessible. The benefits of identifying a computational straightforward LPV input-output (IO) model-that has an equivalent SS representation with static, affine dependency-is combined with an LPV-SS model order reduction. To increase practical relevance of the proposed scheme, in this paper, we present a computational attractive model order reduction scheme based on the LPV Ho-Kalman like realization scheme. We analyze the computational complexity and scalability of our method and compare its benefits to the PBSIDopt scheme. Two examples are provided to demonstrate that our introduced approach performs similar to PBSIDopt in a numerical example and outperforms the PBSIDopt on measurements of a real world system, the air-path system of a gasoline engine.

Originele taal-2Engels
Titel2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's3575-3581
Aantal pagina's7
Volume2018-January
ISBN van elektronische versie978-1-5090-2873-3
ISBN van geprinte versie978-1-5090-2874-0
DOI's
StatusGepubliceerd - 18 jan 2018
Evenement56th IEEE Conference on Decision and Control (CDC 2017), 12-15 December 2017, Melbourne, Australia - Melbourne, Australië
Duur: 12 dec 201715 dec 2017
Congresnummer: 56
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8253407

Congres

Congres56th IEEE Conference on Decision and Control (CDC 2017), 12-15 December 2017, Melbourne, Australia
Verkorte titelCDC 2017
LandAustralië
StadMelbourne
Periode12/12/1715/12/17
Internet adres

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  • Citeer dit

    Schulz, E., Cox, P. B., Toth, R., & Werner, H. (2018). LPV state-space identification via IO methods and efficient model order reduction in comparison with subspace methods. In 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017 (Vol. 2018-January, blz. 3575-3581). Piscataway: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CDC.2017.8264184