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Continuous-time model identification of time-varying systems using non-uniformly sampled data

Johansson, Rolf LU (2016) 2016 IEEE Conference on Control Applications, CCA 2016 In 2016 IEEE Conference on Control Applications, CCA 2016 p.780-785
Abstract

This contribution reviews theory, algorithms, and validation results for system identification of continuous-time models from finite non-uniformly sampled input-output sequences. The algorithms developed are autoregressive methods, and methods of 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. Next, algorithms... (More)

This contribution reviews theory, algorithms, and validation results for system identification of continuous-time models from finite non-uniformly sampled input-output sequences. The algorithms developed are autoregressive methods, and methods of 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. Next, algorithms and validation results are presented for system identification of continuous-time models from finite non-uniformly sampled input-output sequences suitable for parameter tracking of time-varying parameters. The algorithms developed are methods of model identification and stochastic realization adapted to the continuous-time model context using non-uniformly sampled input-output data.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
2016 IEEE Conference on Control Applications, CCA 2016
pages
6 pages
publisher
Institute of Electrical and Electronics Engineers Inc.
conference name
2016 IEEE Conference on Control Applications, CCA 2016
external identifiers
  • scopus:84994304776
ISBN
9781509007554
DOI
10.1109/CCA.2016.7587913
language
English
LU publication?
yes
id
4892bd97-4e2f-446d-b279-0a845149a528
date added to LUP
2016-12-07 10:52:52
date last changed
2017-01-01 08:42:16
@inproceedings{4892bd97-4e2f-446d-b279-0a845149a528,
  abstract     = {<p>This contribution reviews theory, algorithms, and validation results for system identification of continuous-time models from finite non-uniformly sampled input-output sequences. The algorithms developed are autoregressive methods, and methods of 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. Next, algorithms and validation results are presented for system identification of continuous-time models from finite non-uniformly sampled input-output sequences suitable for parameter tracking of time-varying parameters. The algorithms developed are methods of model identification and stochastic realization adapted to the continuous-time model context using non-uniformly sampled input-output data.</p>},
  author       = {Johansson, Rolf},
  booktitle    = {2016 IEEE Conference on Control Applications, CCA 2016},
  isbn         = {9781509007554},
  language     = {eng},
  month        = {10},
  pages        = {780--785},
  publisher    = {Institute of Electrical and Electronics Engineers Inc.},
  title        = {Continuous-time model identification of time-varying systems using non-uniformly sampled data},
  url          = {http://dx.doi.org/10.1109/CCA.2016.7587913},
  year         = {2016},
}