Continuous-Time Model Identification Using Non-Uniformly Sampled Data

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.

• Author: Rolf Johansson; Marzia Cescon; Fredrik Ståhl
• Department/s: Department of Automatic Control; ELLIIT: the Linköping-Lund initiative on IT and mobile communication
• Publishing year: 2013
• Language: English
• Publication/Series: Proc. IEEE AFRICON 2013 Conference
• Document type: Conference paper
• Topic: Control Engineering
• Conference name: IEEE AFRICON 2013 Conference
• Conference date: 2013-09-09 - 2013-09-12
• Conference place: Mauritius
• Status: Published
• Research group: LCCC