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Continuous-time model identification using non-uniformly sampled data

Johansson, Rolf LU ; Cescon, Marzia LU and Ståhl, Fredrik LU (2013) IEEE AFRICON 2013 Conference
Abstract
This contribution reviews theory, algorithms, and validation results for system identification of continuous-time state-space models from finite input- output sequences. The algorithms developed are autoregressive methods, methods of subspace-based 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... (More)
This contribution reviews theory, algorithms, and validation results for system identification of continuous-time state-space models from finite input- output sequences. The algorithms developed are autoregressive methods, methods of subspace-based 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. Next, theory, algorithms and validation results are presented for system identification of continuous-time state-space models from finite non-uniformly sampled input-output sequences. 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. (Less)
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Contribution to conference
publication status
published
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conference name
IEEE AFRICON 2013 Conference
external identifiers
  • Scopus:84897426161
language
English
LU publication?
yes
id
d09ea4fe-b915-48dd-aae0-76110ed4e160 (old id 4076221)
date added to LUP
2013-10-07 11:56:57
date last changed
2016-10-13 04:55:42
@misc{d09ea4fe-b915-48dd-aae0-76110ed4e160,
  abstract     = {This contribution reviews theory, algorithms, and validation results for system identification of continuous-time state-space models from finite input- output sequences. The algorithms developed are autoregressive methods, methods of subspace-based 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. Next, theory, algorithms and validation results are presented for system identification of continuous-time state-space models from finite non-uniformly sampled input-output sequences. 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.},
  author       = {Johansson, Rolf and Cescon, Marzia and Ståhl, Fredrik},
  language     = {eng},
  title        = {Continuous-time model identification using non-uniformly sampled data},
  year         = {2013},
}