kalman

actuator

microgrids

near-field

fault estimation

kalman filtering

estimation_61083

detection filter

correntropy

few-mode

robot_59908

mowing

weaned

extended kalman

seminiferous

two-sided

nonlinear filtering

phages_17272

Automatica

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.

Adaptive Kalman filter for actuator fault diagnosis