Adaptive Kalman filter for actuator fault diagnosis. (July 2018)

• Record Type: Journal Article
• Authors: Zhang, Qinghua
• Is Part Of: Automatica. Volume 93(2018)
• Page Start: 333
• Page End: 342
• Publication Date: 2018-07
• Subjects: Adaptive observer -- Joint state-parameter estimation -- Fault diagnosis -- Kalman filter; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2018.03.075
• Languages: English
• ISSNs: 0005-1098

Abstract

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. In addition to the minimum variance property of the combined state and parameter estimation errors, it is shown that the parameter estimation within the proposed adaptive Kalman filter is equivalent to the recursive least squares algorithm formulated for a fictive regression problem. Numerical examples are presented to illustrate the performance of the proposed algorithm.