This work presents a model-based approach for the detection, estimation and accommodation of control actuator faults in dynamic processes controlled using multi-rate sampled state measurements. Initially, a model-based state feedback controller that employs an inter-sample model predictor to compensate for the unavailability of continuous state measurements is designed. Characterizations of both the stability and performance properties of the multi-rate sampled-data closed-loop system are obtained and expressed in terms of the measurement sampling rates, the magnitudes of the faults, and the various predictor and controller design parameters. A parameter estimator that estimates the magnitudes of the faults by minimizing the error between the estimated and sampled states over a moving horizon is then introduced and used to determine the fault or health status of the control actuators at the sampling times. The identification of faults triggers a fault accommodation scheme that adjusts selected predictor and controller design parameters to simultaneously preserve closed-loop stability and optimize a pre-defined performance metric. The implementation of the developed approach is illustrated using a simulated model of a solid oxide fuel cell system subject to both stability and performance-degrading actuator faults. A discussion of some of the intricacies of the multi-rate sampling scheme and how they impact the practical implementation of the fault identification and accommodation schemes is presented.
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