Predictive Kalman filter-based fault estimator and control for sampled-data linear time-varying systems

The universal state-space adaptive observer-based fault diagnosis/estimator and the high-gain property tracker for sampled-data linear slowly time-varying system with unanticipated decay factors in actuators/system states are proposed in this paper. An improved Kalman filter-based adaptive observer is proposed in this paper to achieve better estimation-based performance recovery than the conventional one. A residual generation scheme and a mechanism for auto-tuning switched gain is presented, so that the proposed methodology is applicable for the fault detection and diagnosis (FDD) for actuator and state failures to yield high tracking performance recovery. For practical implementation, this paper also takes advantage of the merit of digital redesign methodology to convert a theoretically well-designed analog controller/observer with a high-gain property into its corresponding low-gain digital controller/observer without possibly losing the high tracking/estimation as well as FDD performance recovery.