Stochastic Theory of Continuous-Time State-Space Identification

Rolf Johansson; Michel Verhaegen; C. T. Chou

doi:10.1109/CDC.1997.657856

Stochastic Theory of Continuous-Time State-Space Identification

Rolf Johansson, Michel Verhaegen, C. T. Chou

Department of Automatic Control

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

Abstract

Presents theory, algorithms and validation results for system identification of continuous-time state-space models from finite input-output sample sequences. The algorithms developed are methods of subspace model identification and stochastic realization adapted to the continuous-time context. The resulting model can be decomposed into an input-output model and a stochastic innovations model. Using the Riccati equation, we have designed a procedure to provide a reduced-order stochastic model that is minimal with respect to system order as well as the number of stochastic inputs thereby avoiding several problems appearing in standard application of stochastic realization to the model validation problem

Original language	English
Title of host publication	Proceedings of the 36th IEEE Conference on Decision and Control
Publisher	IEEE - Institute of Electrical and Electronics Engineers Inc.
Pages	1866-1871
DOIs	https://doi.org/10.1109/CDC.1997.657856
Publication status	Published - 1997
Event	36th IEEE Conference on Decision and Control, 1997 - San Diego, CA, San Diego, California, United States Duration: 1997 Dec 12 → 1997 Dec 12 Conference number: 36

Conference

Conference	36th IEEE Conference on Decision and Control, 1997
Country/Territory	United States
City	San Diego, California
Period	1997/12/12 → 1997/12/12

Subject classification (UKÄ)

Control Engineering

Access to Document

10.1109/CDC.1997.657856

Cite this

@inproceedings{07badc72c8534be199c3ffb0622fbc81,

title = "Stochastic Theory of Continuous-Time State-Space Identification",

abstract = "Presents theory, algorithms and validation results for system identification of continuous-time state-space models from finite input-output sample sequences. The algorithms developed are methods of subspace model identification and stochastic realization adapted to the continuous-time context. The resulting model can be decomposed into an input-output model and a stochastic innovations model. Using the Riccati equation, we have designed a procedure to provide a reduced-order stochastic model that is minimal with respect to system order as well as the number of stochastic inputs thereby avoiding several problems appearing in standard application of stochastic realization to the model validation problem",

author = "Rolf Johansson and Michel Verhaegen and Chou, {C. T.}",

year = "1997",

doi = "10.1109/CDC.1997.657856",

language = "English",

pages = "1866--1871",

booktitle = "Proceedings of the 36th IEEE Conference on Decision and Control",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "36th IEEE Conference on Decision and Control, 1997 ; Conference date: 12-12-1997 Through 12-12-1997",

}

Johansson, R, Verhaegen, M & Chou, CT 1997, Stochastic Theory of Continuous-Time State-Space Identification. in Proceedings of the 36th IEEE Conference on Decision and Control. IEEE - Institute of Electrical and Electronics Engineers Inc., pp. 1866-1871, 36th IEEE Conference on Decision and Control, 1997, San Diego, California, United States, 1997/12/12. https://doi.org/10.1109/CDC.1997.657856

Stochastic Theory of Continuous-Time State-Space Identification. / Johansson, Rolf; Verhaegen, Michel; Chou, C. T.
Proceedings of the 36th IEEE Conference on Decision and Control. IEEE - Institute of Electrical and Electronics Engineers Inc., 1997. p. 1866-1871.

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

TY - GEN

T1 - Stochastic Theory of Continuous-Time State-Space Identification

AU - Johansson, Rolf

AU - Verhaegen, Michel

AU - Chou, C. T.

N1 - Conference code: 36

PY - 1997

Y1 - 1997

N2 - Presents theory, algorithms and validation results for system identification of continuous-time state-space models from finite input-output sample sequences. The algorithms developed are methods of subspace model identification and stochastic realization adapted to the continuous-time context. The resulting model can be decomposed into an input-output model and a stochastic innovations model. Using the Riccati equation, we have designed a procedure to provide a reduced-order stochastic model that is minimal with respect to system order as well as the number of stochastic inputs thereby avoiding several problems appearing in standard application of stochastic realization to the model validation problem

AB - Presents theory, algorithms and validation results for system identification of continuous-time state-space models from finite input-output sample sequences. The algorithms developed are methods of subspace model identification and stochastic realization adapted to the continuous-time context. The resulting model can be decomposed into an input-output model and a stochastic innovations model. Using the Riccati equation, we have designed a procedure to provide a reduced-order stochastic model that is minimal with respect to system order as well as the number of stochastic inputs thereby avoiding several problems appearing in standard application of stochastic realization to the model validation problem

U2 - 10.1109/CDC.1997.657856

DO - 10.1109/CDC.1997.657856

M3 - Paper in conference proceeding

SP - 1866

EP - 1871

BT - Proceedings of the 36th IEEE Conference on Decision and Control

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - 36th IEEE Conference on Decision and Control, 1997

Y2 - 12 December 1997 through 12 December 1997

ER -

Stochastic Theory of Continuous-Time State-Space Identification

Abstract

Conference

Subject classification (UKÄ)

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