Webbläsaren som du använder stöds inte av denna webbplats. Alla versioner av Internet Explorer stöds inte längre, av oss eller Microsoft (läs mer här: * https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Var god och använd en modern webbläsare för att ta del av denna webbplats, som t.ex. nyaste versioner av Edge, Chrome, Firefox eller Safari osv.

Continuous-Time Model Identification Using Non-Uniformly Sampled Data

In English

Författare

Summary, in English

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.

Publiceringsår

2013

Språk

Engelska

Publikation/Tidskrift/Serie

Proc. IEEE AFRICON 2013 Conference

Dokumenttyp

Konferensbidrag

Ämne

  • Control Engineering

Conference name

IEEE AFRICON 2013 Conference

Conference date

2013-09-09 - 2013-09-12

Conference place

Mauritius

Status

Published

Forskningsgrupp

  • LCCC