Data Informativity for the Open-Loop Identification of MIMO Systems in the Prediction Error Framework - CNRS - Centre national de la recherche scientifique Accéder directement au contenu
Article Dans Une Revue Automatica Année : 2020

Data Informativity for the Open-Loop Identification of MIMO Systems in the Prediction Error Framework

Résumé

In Prediction Error identification, to obtain a consistent estimate of the true system, it is crucial that the input excitation yields informative data with respect to the chosen model structure. We consider in this paper the data informativity property for the identification of a Multiple-Input Multiple-Output system in open loop and we derive conditions to check whether a given input vector will yield informative data with respect to the chosen model structure. We do that for the classical model structures used in prediction-error identification and for the classical types of input vectors, i.e., input vectors whose elements are either multisines or filtered white noises.
Fichier principal
Vignette du fichier
info_OL_HAL_v1.pdf (243.45 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02305057 , version 1 (03-10-2019)

Identifiants

Citer

Kévin Colin, Xavier Bombois, Laurent Bako, Federico Morelli. Data Informativity for the Open-Loop Identification of MIMO Systems in the Prediction Error Framework. Automatica, 2020, ⟨10.1016/j.automatica.2020.109000⟩. ⟨hal-02305057⟩
176 Consultations
152 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More