Subspace identification of continuous-time models using generalized orthonormal bases

Chengpu Yu; Jie Chen; Lennart Ljung; Michel Verhaegen

doi:10.1109/CDC.2017.8264440

Subspace identification of continuous-time models using generalized orthonormal bases

Chengpu Yu, Jie Chen, Lennart Ljung, Michel Verhaegen

Team Raf Van de Plas

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

4 Citations (Scopus)

80 Downloads (Pure)

Abstract

The continuous-time subspace identification using state-variable filtering has been investigated for a long time. Due to the simple orthogonal basis functions that were adopted by the existing methods, the identification performance is quite sensitive to the selection of the system-dynamic parameter associated with an orthogonal basis. To cope with this problem, a subspace identification method using generalized orthonormal (Takenaka-Malmquist) basis functions is developed, which has the potential to perform better than the existing state-variable filtering methods since the adopted Takenaka-Malmquist basis has more degree of freedom in selecting the system-dynamic parameters. As a price for the flexibility of the generalized orthonormal bases, the transformed state-space model is time-varying or parameter-varying which cannot be identified using traditional subspace identification methods. To this end, a new subspace identification algorithm is developed by exploiting the structural properties of the time-variant system matrices, which is then validated by numerical simulations.

Original language	English
Title of host publication	Proceedings of the 2017 IEEE 56th Annual Conference on Decision and Control
Editors	A. Astolfi et al
Place of Publication	Piscataway, NJ, USA
Publisher	IEEE
Pages	5280-5285
ISBN (Electronic)	978-150902873-3
DOIs	https://doi.org/10.1109/CDC.2017.8264440
Publication status	Published - 2017
Event	CDC 2017: 56th IEEE Annual Conference on Decision and Control - Melbourne, Australia Duration: 12 Dec 2017 → 15 Dec 2017 http://cdc2017.ieeecss.org/

Conference

Conference	CDC 2017: 56th IEEE Annual Conference on Decision and Control
Country/Territory	Australia
City	Melbourne
Period	12/12/17 → 15/12/17
Other	The CDC is recognized as the premier scientific and engineering conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, systems and control, and related areas.
Internet address	http://cdc2017.ieeecss.org/

Bibliographical note

Accepted Author Manuscript

Access to Document

10.1109/CDC.2017.8264440

ct_sysid_chengpuAccepted author manuscript, 162 KB

Cite this

@inproceedings{d08f55d75429414a84e844ca0ce4f630,

title = "Subspace identification of continuous-time models using generalized orthonormal bases",

abstract = "The continuous-time subspace identification using state-variable filtering has been investigated for a long time. Due to the simple orthogonal basis functions that were adopted by the existing methods, the identification performance is quite sensitive to the selection of the system-dynamic parameter associated with an orthogonal basis. To cope with this problem, a subspace identification method using generalized orthonormal (Takenaka-Malmquist) basis functions is developed, which has the potential to perform better than the existing state-variable filtering methods since the adopted Takenaka-Malmquist basis has more degree of freedom in selecting the system-dynamic parameters. As a price for the flexibility of the generalized orthonormal bases, the transformed state-space model is time-varying or parameter-varying which cannot be identified using traditional subspace identification methods. To this end, a new subspace identification algorithm is developed by exploiting the structural properties of the time-variant system matrices, which is then validated by numerical simulations.",

author = "Chengpu Yu and Jie Chen and Lennart Ljung and Michel Verhaegen",

note = "Accepted Author Manuscript; CDC 2017: 56th IEEE Annual Conference on Decision and Control ; Conference date: 12-12-2017 Through 15-12-2017",

year = "2017",

doi = "10.1109/CDC.2017.8264440",

language = "English",

pages = "5280--5285",

editor = "{Astolfi et al}, A.",

booktitle = "Proceedings of the 2017 IEEE 56th Annual Conference on Decision and Control",

publisher = "IEEE",

address = "United States",

url = "http://cdc2017.ieeecss.org/",

}

Yu, C, Chen, J, Ljung, L & Verhaegen, M 2017, Subspace identification of continuous-time models using generalized orthonormal bases. in A Astolfi et al (ed.), Proceedings of the 2017 IEEE 56th Annual Conference on Decision and Control. IEEE, Piscataway, NJ, USA, pp. 5280-5285, CDC 2017: 56th IEEE Annual Conference on Decision and Control, Melbourne, Australia, 12/12/17. https://doi.org/10.1109/CDC.2017.8264440

Subspace identification of continuous-time models using generalized orthonormal bases. / Yu, Chengpu; Chen, Jie; Ljung, Lennart et al.
Proceedings of the 2017 IEEE 56th Annual Conference on Decision and Control. ed. / A. Astolfi et al. Piscataway, NJ, USA: IEEE, 2017. p. 5280-5285.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Subspace identification of continuous-time models using generalized orthonormal bases

AU - Yu, Chengpu

AU - Chen, Jie

AU - Ljung, Lennart

AU - Verhaegen, Michel

N1 - Accepted Author Manuscript

PY - 2017

Y1 - 2017

N2 - The continuous-time subspace identification using state-variable filtering has been investigated for a long time. Due to the simple orthogonal basis functions that were adopted by the existing methods, the identification performance is quite sensitive to the selection of the system-dynamic parameter associated with an orthogonal basis. To cope with this problem, a subspace identification method using generalized orthonormal (Takenaka-Malmquist) basis functions is developed, which has the potential to perform better than the existing state-variable filtering methods since the adopted Takenaka-Malmquist basis has more degree of freedom in selecting the system-dynamic parameters. As a price for the flexibility of the generalized orthonormal bases, the transformed state-space model is time-varying or parameter-varying which cannot be identified using traditional subspace identification methods. To this end, a new subspace identification algorithm is developed by exploiting the structural properties of the time-variant system matrices, which is then validated by numerical simulations.

AB - The continuous-time subspace identification using state-variable filtering has been investigated for a long time. Due to the simple orthogonal basis functions that were adopted by the existing methods, the identification performance is quite sensitive to the selection of the system-dynamic parameter associated with an orthogonal basis. To cope with this problem, a subspace identification method using generalized orthonormal (Takenaka-Malmquist) basis functions is developed, which has the potential to perform better than the existing state-variable filtering methods since the adopted Takenaka-Malmquist basis has more degree of freedom in selecting the system-dynamic parameters. As a price for the flexibility of the generalized orthonormal bases, the transformed state-space model is time-varying or parameter-varying which cannot be identified using traditional subspace identification methods. To this end, a new subspace identification algorithm is developed by exploiting the structural properties of the time-variant system matrices, which is then validated by numerical simulations.

UR - http://www.scopus.com/inward/record.url?scp=85046165014&partnerID=8YFLogxK

UR - http://resolver.tudelft.nl/uuid:d08f55d7-5429-414a-84e8-44ca0ce4f630

U2 - 10.1109/CDC.2017.8264440

DO - 10.1109/CDC.2017.8264440

M3 - Conference contribution

AN - SCOPUS:85046165014

SP - 5280

EP - 5285

BT - Proceedings of the 2017 IEEE 56th Annual Conference on Decision and Control

A2 - Astolfi et al, A.

PB - IEEE

CY - Piscataway, NJ, USA

T2 - CDC 2017: 56th IEEE Annual Conference on Decision and Control

Y2 - 12 December 2017 through 15 December 2017

ER -

Subspace identification of continuous-time models using generalized orthonormal bases

Abstract

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this