Subspace identification of continuous-time models using generalized orthonormal bases

Subspace identification of continuous-time models using generalized orthonormal bases

Yu, C. (Beijing Institute of Technology)
Chen, Jie (Beijing Institute of Technology)
Ljung, Lennart (Linköping University)
Verhaegen, M.H.G. (TU Delft Team Raf Van de Plas)

Astolfi et al, A. (editor)

2017

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.

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

https://doi.org/10.1109/CDC.2017.8264440

IEEE, Piscataway, NJ, USA

978-150902873-3

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

CDC 2017: 56th IEEE Annual Conference on Decision and Control, 2017-12-12 → 2017-12-15, Melbourne, Australia

Accepted Author Manuscript

Institutional Repository

conference paper

© 2017 C. Yu, Jie Chen, Lennart Ljung, M.H.G. Verhaegen