Adaptive Kalman Filter for Actuator Fault Diagnosis

Qinghua Zhang (1)

1 I4S - Statistical Inference for Structural Health Monitoring

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.

Mots clés: FDI; adaptive observer; joint state-parameter estimation; LTV/LPV systems

Domaines: Automatique

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⟩