Stochastic Theory of Continuous-Time State-Space Identification

Summary, in English

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

• Author: Rolf Johansson; Michel Verhaegen; C. T. Chou
• Department/s: Department of Automatic Control
• Publishing year: 1997
• Language: English
• Pages: 1866-1871
• Publication/Series: Proceedings of the 36th IEEE Conference on Decision and Control
• Links: http://dx.doi.org/10.1109/CDC.1997.657856
• Document type: Conference paper
• Publisher: IEEE - Institute of Electrical and Electronics Engineers Inc.
• Topic: Control Engineering
• Conference name: 36th IEEE Conference on Decision and Control, 1997
• Conference date: 1997-12-12 - 1997-12-12
• Conference place: San Diego, California, United States
• Status: Published