Identification of continuous-time errors-in-variables models

• Published in: Automatica (Oxford) Vol. 42; no. 9; pp. 1477 - 1490
• Main Authors: Mahata, Kaushik, Garnier, Hugues
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01-09-2006; Elsevier
• Subjects: Applied sciences; Automatic; Computer science; control theory; systems; Continuous-time models; Control theory. Systems; Engineering Sciences; Errors-in-variables; Exact sciences and technology; Linear estimation; Modelling and identification; System identification; System identification; Linear estimation; Continuous-time models; Errors-in-variables; Input error; Sampled system; Output error; Error estimation; Continuous time; Linear time; Modeling; Search algorithm; Filter; Asymptotic approximation; Dynamic model; system identification; linear estimation; continuous-time models
• ISSN: 0005-1098; 1873-2836
• DOI: 10.1016/j.automatica.2006.04.012

A novel direct approach for identifying continuous-time linear dynamic errors-in-variables models is presented in this paper. The effects of the noise on the state-variable filter outputs are analyzed. Subsequently, a few algorithms to obtain consistent continuous-time parameter estimates in the errors-in-variables framework are derived. It is also possible to design search-free algorithms within our framework. The algorithms can be used for non-uniformly sampled data. The asymptotic distributions of the estimates are derived. The performances of the proposed algorithms are illustrated with some numerical simulation examples.