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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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.

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
StatePublished - 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

Fingerprint

Model Order Reduction
Subspace Methods
Identification (control systems)
State Space
Output
State-space Model
State-space Representation
Discrete-time Model
Gasoline
Scalability
Computational complexity
Computational Model
Engines
Computational Complexity
Engine
State space
Air
Numerical Examples
Path
Demonstrate

Cite this

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, pp. 3575-3581). Piscataway: Institute of Electrical and Electronics Engineers. DOI: 10.1109/CDC.2017.8264184
Schulz, E. ; Cox, P.B. ; Toth, R. ; Werner, H./ LPV state-space identification via IO methods and efficient model order reduction in comparison with subspace methods. 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. Vol. 2018-January Piscataway : Institute of Electrical and Electronics Engineers, 2018. pp. 3575-3581
@inproceedings{16cc060f0d2d45ce95d19fba46086dbc,
title = "LPV state-space identification via IO methods and efficient model order reduction in comparison with subspace methods",
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.",
author = "E. Schulz and P.B. Cox and R. Toth and H. Werner",
year = "2018",
month = "1",
day = "18",
doi = "10.1109/CDC.2017.8264184",
language = "English",
isbn = "978-1-5090-2874-0",
volume = "2018-January",
pages = "3575--3581",
booktitle = "2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017",
publisher = "Institute of Electrical and Electronics Engineers",
address = "United States",

}

Schulz, E, Cox, PB, 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, Institute of Electrical and Electronics Engineers, Piscataway, pp. 3575-3581, 56th IEEE Conference on Decision and Control (CDC 2017), Melbourne, Australia, 12/12/17. DOI: 10.1109/CDC.2017.8264184

LPV state-space identification via IO methods and efficient model order reduction in comparison with subspace methods. / Schulz, E.; Cox, P.B.; Toth, R.; Werner, H.

2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. Vol. 2018-January Piscataway : Institute of Electrical and Electronics Engineers, 2018. p. 3575-3581.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

TY - GEN

T1 - LPV state-space identification via IO methods and efficient model order reduction in comparison with subspace methods

AU - Schulz,E.

AU - Cox,P.B.

AU - Toth,R.

AU - Werner,H.

PY - 2018/1/18

Y1 - 2018/1/18

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85046148897&partnerID=8YFLogxK

U2 - 10.1109/CDC.2017.8264184

DO - 10.1109/CDC.2017.8264184

M3 - Conference contribution

SN - 978-1-5090-2874-0

VL - 2018-January

SP - 3575

EP - 3581

BT - 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

ER -

Schulz E, Cox PB, Toth R, Werner H. 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. Piscataway: Institute of Electrical and Electronics Engineers. 2018. p. 3575-3581. Available from, DOI: 10.1109/CDC.2017.8264184