Subspace identification of continuous-time models using generalized orthonormal bases

Chengpu Yu, Jie Chen, Lennart Ljung, Michel Verhaegen

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
14 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 languageEnglish
Title of host publicationProceedings of the 2017 IEEE 56th Annual Conference on Decision and Control
EditorsA. Astolfi et al
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Pages5280-5285
ISBN (Electronic)978-150902873-3
DOIs
Publication statusPublished - 2017
EventCDC 2017: 56th IEEE Annual Conference on Decision and Control - Melbourne, Australia
Duration: 12 Dec 201715 Dec 2017
http://cdc2017.ieeecss.org/

Conference

ConferenceCDC 2017: 56th IEEE Annual Conference on Decision and Control
CountryAustralia
CityMelbourne
Period12/12/1715/12/17
OtherThe 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.
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  • Cite this

    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 (pp. 5280-5285). IEEE. https://doi.org/10.1109/CDC.2017.8264440