(615c) Model-Based Fault Detection and Fault-Tolerant Control of Process Systems Using Sampled and Delayed Measurements

A central problem at the interface of process control and operations is the development of systematic methods for the diagnosis and handling of faults. The motivation for studying this problem stems in part from the vulnerability of automated industrial processes to malfunctions in the control actuators, measurement sensors and process equipment; as well as the increased emphasis placed on safety, reliability and profitability in the operation of industrial processes. It is well understood that faults can lead to substantial degradation in the process performance, and may even lead to complete breakdown of process operation if not accounted for in the control system design. These realizations have motivated a significant and growing body of research work on fault diagnosis and fault-tolerant control of process systems over the past few decades in both the academic and industrial circles. In most of the existing methods, however, the monitoring and fault-tolerant control problems are typically formulated and addressed within the classical feedback control paradigm where the output of the process is assumed to be passed directly to the controller, which then generates the control input and in turn passes it directly back to the process.

In practice, this paradigm often needs to be re-examined, in part because of the increasing complexity of the interface between the controller and the process which features additional information-processing devices that should be accounted for in the design of the monitoring and control systems. For example, with the advent of networked control systems in recent years and the emergence of applications involving large numbers of networked sensors and actuators, the example of digitally interconnected systems that are monitored and controlled through finite communication channels (i.e., channels capable of transmitting only discrete information between the process and the controller) is becoming commonplace. In such systems, inherent limitations on the information transmission and processing capabilities of the measurement system and the communication medium can erode the diagnostic and fault-tolerance capabilities if not properly handled in the design of the monitoring and control systems. Issues such as resource constraints, processing and communication delays, data sampling and losses, measurement quantization and real-time scheduling constraints, challenge many of the assumptions in traditional process monitoring and control methods and need to be integrated explicitly in the fault-tolerant control system design.

In this work, we focus on the development of a model-based framework for actuator fault detection and reconfiguration in process systems with discretely-sampled and delayed measurements. To ensure fault-tolerance, a number of control configurations are assumed to be available only one of which is used at any given time while the rest are kept dormant for possible use as backup in the event of faults. Initially, an observer-based output feedback controller is designed to stabilize the plant in the absence of faults. 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. The model state is then updated when measurements are transmitted at discrete times. To compensate for the measurement delay, we include within the control system also 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, will generate the control signal for the plant. 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 obtained from this analysis is used as the basis for deriving appropriate rules for fault detection and control system reconfiguration. The idea is to use the state observer as a fault detection filter and compare its output with the estimate of the current plant output generated by the propagation unit at the sampling times. The discrepancy is used as a residual and compared against a time-varying alarm threshold obtained from the stability analysis to determine the fault or health status of the control actuators. Once a fault is detected in the operating control configuration, the control system is prompted to switch to one of the feasible fall-back control configurations under the given measurement sampling rate and delay to preserve stability and minimize performance deterioration. Finally, the proposed fault-tolerant control framework is illustrated using a chemical process example.