Continuous-Time Model Identification Using Non-Uniformly Sampled Data

Rolf Johansson, Marzia Cescon, Fredrik Ståhl

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceedingPeer review

Sammanfattning

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.
Originalspråkengelska
Titel på värdpublikationProc. IEEE AFRICON 2013 Conference
StatusPublished - 2013
EvenemangIEEE AFRICON 2013 Conference - Mauritius, Mauritius
Varaktighet: 2013 sep. 92013 sep. 12

Konferens

KonferensIEEE AFRICON 2013 Conference
Land/TerritoriumMauritius
Period2013/09/092013/09/12

Ämnesklassifikation (UKÄ)

  • Reglerteknik

Fingeravtryck

Utforska forskningsämnen för ”Continuous-Time Model Identification Using Non-Uniformly Sampled Data”. Tillsammans bildar de ett unikt fingeravtryck.

Citera det här