Unknown Input Proportional Multiple-Integral Observer Design for Linear Descriptor Systems: Application to State and Fault Estimation - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Automatic Control Année : 2005

Unknown Input Proportional Multiple-Integral Observer Design for Linear Descriptor Systems: Application to State and Fault Estimation

Damien Koenig

Résumé

In this paper, the problem of observer design for linear descriptor systems with faults and unknown inputs is considered. First, it is considered that the fault vector function f is s times piecewise continuously differentiable. If the s^{th} time derivative of f is null, then s integral actions are included into a Luenberger observer, which is designed such that it estimates simultaneously the state, the fault and its finite derivatives face to unknown inputs. Second, when the fault is not time piecewise continuously differentiable but bounded (like actuator noise) or s^{th} time derivative of fault is not null but bounded too, a high gain observer is derived to attenuate the fault impact in estimation errors. The considered faults may be unbounded, may not be determinist, and faults and unknown inputs may affect the state dynamic and plant outputs. Sufficient conditions for the existence of such observer are given. Results are illustrated with a differential algebraic power system.

Domaines

Automatique
Fichier principal
Vignette du fichier
TAC05_UIPMIO.pdf (285.42 Ko) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte
Loading...

Dates et versions

hal-00152225 , version 1 (06-06-2007)

Identifiants

  • HAL Id : hal-00152225 , version 1

Citer

Damien Koenig. Unknown Input Proportional Multiple-Integral Observer Design for Linear Descriptor Systems: Application to State and Fault Estimation. IEEE Transactions on Automatic Control, 2005, 50 (2), pp.212-217. ⟨hal-00152225⟩

Collections

UGA CNRS LAG
188 Consultations
888 Téléchargements

Partager

Gmail Facebook X LinkedIn More