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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Abstract

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.

Original languageEnglish
Title of host publication2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages3575-3581
Number of pages7
Volume2018-January
ISBN (Electronic)978-1-5090-2873-3
ISBN (Print)978-1-5090-2874-0
DOIs
Publication statusPublished - 18 Jan 2018
Event56th IEEE Conference on Decision and Control (CDC 2017) - Melbourne, Australia
Duration: 12 Dec 201715 Dec 2017
Conference number: 56
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8253407

Conference

Conference56th IEEE Conference on Decision and Control (CDC 2017)
Abbreviated titleCDC 2017
CountryAustralia
CityMelbourne
Period12/12/1715/12/17
Internet address

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