Identification and Accommodation of Control Actuator Faults Using Multi-rate Sampled Measurements

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. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.