Model-Based Fault Detection and Fault-Tolerant Control of Process Systems with Sampled and Delayed Measurements

Abstract

AbstractThis paper presents a model-based framework for actuator fault detection and reconfiguration in process systems with discretely-sampled and delayed output measurements. An observer-based output feedback controller that enforces closed-loop stability in the absence of faults is initially designed. To compensate for the lack of continuous measurements, an inter-sample model predictor is included within the control system to provide the observer with an estimate of the output when measurements are not available from the sensors. Measurement delays are compensated for by means of a propagation unit that uses the plant model and the past values of the control input to calculate an estimate of the current output from the received delayed measurements. This estimate is then used to update the inter-sample model predictor which, together with the controller, generates the control signal for the process. By formulating the closed-loop system as a combined discrete-continuous system, an explicit characterization of the minimum allowable sampling rate that guarantees stability in the absence of faults is obtained in terms of the plant-model mismatch, the controller and observer design parameters, the size of the measurement delay, and the choice of the control configuration. The characteristic fault-free closed-loop behavior is used as the basis for deriving (1) a time-varying alarm threshold on the residual of the fault detection filter, and (2) a controller reconfiguration law that determines the feasible fall-back control configurations that preserve closed-loop stability. Finally, the design and implementation of the integrated monitoring and fault-tolerant control architecture are demonstrated using a chemical process example.