Continuous-Time Model Identification of Time-Varying Systems Using Non-Uniformly Sampled Data

This contribution reviews theory, algorithms, and validation results for system identification of continuous-time models from finite non-uniformly sampled input-output sequences. The algorithms developed are autoregressive methods, and methods of 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, algorithms and validation results are presented for system identification of continuous-time models from finite non-uniformly sampled input-output sequences suitable for parameter tracking of time-varying parameters. 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.