Stochastic theory of continuous-time state-space identification

Author

Johansson, R. ; Verhaegen, Michel ; Chou, Chun Tung

Author_Institution

Dept. of Autom. Control, Lund Univ., Sweden

Volume

47

Issue

1

fYear

1999

fDate

1/1/1999 12:00:00 AM

Firstpage

41

Lastpage

51

Abstract

This paper presents theory, algorithms, and validation results for system identification of continuous-time state-space models from finite input-output 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

Keywords

Riccati equations; continuous time systems; identification; reduced order systems; sequences; signal processing; state-space methods; stochastic processes; Riccati equation; algorithms; continuous-time state-space identification; finite input-output sequences; input-output model; reduced-order stochastic model; stochastic innovations model; stochastic inputs; stochastic realization; stochastic theory; subspace model identification; system identification; validation problem; Context modeling; Frequency; Helium; Riccati equations; Sampling methods; Signal processing algorithms; Stochastic processes; Stochastic systems; System identification; Technological innovation;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.738238

Filename

738238