Data-based continuous-time model identification of continuous-time dynamic systems is a mature subject. In this contribution, we focus first on a refined instrumental variable method that yields parameter estimates with optimal statistical properties for hybrid continuous-time Box-Jenkins transfer function models. The second part of the paper describes further recent developments of this reliable estimation technique, including its extension to handle non-uniformly sampled data situation, closed-loop and nonlinear model identification. It also discusses how the recently developed methods are implemented in the CONTSID toolbox for Matlab and the advantages of these direct schemes to continuous-time model identification.
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