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Continuous-Time Model Identification and State Estimation Using Non-Uniformly Sampled Data

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Author

Summary, in English

This paper presents theory, algorithms and validation results 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. The resulting model can be decomposed into an input-output model and a stochastic innovations model. For state estimation dynamics, we have designed a procedure to provide separate continuous-time temporal update and error-feedback update based on non-uniformly sampled input-output data. Stochastic onvergence analysis is provided.

Publishing year

2009

Language

English

Publication/Series

Proc. 15th IFAC Symposium on System Identification (SYSID2009)

Document type

Conference paper

Topic

  • Control Engineering

Conference name

15th IFAC Symposium on System Identification

Conference date

2009-06-06 - 2009-06-08

Conference place

Saint-Malo, France

Status

Published

Research group

  • LCCC