Conference Contribution

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

Rolf Johansson


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.

In Proc. 15th IFAC Symposium on System Identification (SYSID2009), July 6 - 8, 2009, Saint-Malo, France, July 2009.

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