Subspace based methods for continuous-time model identification of MIMO systems from filtered sampled data

Guillaume Mercère (1) , Régis Ouvrard (1) , Marion Gilson (2) , Hugues Garnier (2)

1 LAII [Poitiers] - Laboratoire d'automatique et d'informatique industrielle [EA 1219]
2 CRAN - Centre de Recherche en Automatique de Nancy

This article introduces a new identification method for continuous-time MIMO state space models from sampled input output data. The proposed approach consists more precisely in combining filtering techniques with a specific subspace algorithm. Two filtering methods (the reinitialised partial moments and the Poisson moment functionals) are considered to circumvent the time derivative problem inherent in continuous-time modelling. The developed subspace algorithm belongs to the MOESP method family. A particular attention is payed to the construction of the instrumental variable used to supply consistent and accurate estimates in a noisy framework. The benefits of the proposed algorithms in comparison with existing methods are illustrated with a simulation study.

Mots clés: Continuous-time identification; subspace-based methods; instrumental variable

Domaines: Traitement du signal et de l'image [eess.SP]; Automatique / Robotique; Traitement du signal et de l'image [eess.SP]

Guillaume Mercère, Régis Ouvrard, Marion Gilson, Hugues Garnier. Subspace based methods for continuous-time model identification of MIMO systems from filtered sampled data. European Control Conference, ECC'07, Jul 2007, Kos, Greece. pp.CDROM. ⟨hal-00150100⟩