Adaptive Kalman Filter for Actuator Fault Diagnosis

Qinghua Zhang 1
1 I4S - Statistical Inference for Structural Health Monitoring
IFSTTAR/COSYS - Département Composants et Systèmes, Inria Rennes – Bretagne Atlantique
Abstract : An adaptive Kalman filter is proposed in this paper for actuator fault diagnosis in discrete time stochastic time varying systems. By modeling actuator faults as parameter changes, fault diagnosis is performed through joint state-parameter estimation in the considered stochastic framework. Under the classical uniform complete observability-controllability conditions and a persistent excitation condition, the exponential stability of the proposed adaptive Kalman filter is rigorously analyzed. The minimum variance property of the combined state and parameter estimation errors is also demonstrated. Numerical examples are presented to illustrate the performance of the proposed algorithm.
Type de document :
Communication dans un congrès
IFAC 2017 - 20th World Congress of the International Federation of Automatic Control, Jul 2017, Toulouse, France. pp.1-6
Liste complète des métadonnées

Littérature citée [20 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01635108
Contributeur : Qinghua Zhang <>
Soumis le : mardi 14 novembre 2017 - 17:18:37
Dernière modification le : mercredi 11 avril 2018 - 02:00:30
Document(s) archivé(s) le : jeudi 15 février 2018 - 13:43:14

Fichier

adKF.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01635108, version 1

Collections

Citation

Qinghua Zhang. Adaptive Kalman Filter for Actuator Fault Diagnosis. IFAC 2017 - 20th World Congress of the International Federation of Automatic Control, Jul 2017, Toulouse, France. pp.1-6. 〈hal-01635108〉

Partager

Métriques

Consultations de la notice

274

Téléchargements de fichiers

137