in Wiley InterScience (www.interscience.wiley.com).The problem of control of nonlinear process systems subject to input constraints and sensor faults (complete failure or intermittent unavailability of measurements) is considered. A fault-tolerant controller is designed that utilizes reconfiguration (switching to an alternate control configuration) in a way that accounts for the process nonlinearity, the presence of constraints and the occurrence of sensor faults. To clearly illustrate the importance of accounting for the presence of input constraints, first the problem of sensor faults that necessitate sensor recovery to maintain closed-loop stability is considered. We address the problem of determining, based on stability region characterizations for the candidate control configurations, which control configuration should be activated (reactivating the primary control configuration may not preserve stability) after the sensor is rectified. We then consider the problem of asynchronous measurements, that is of intermittent unavailability of measurements. To address this problem, the stability region (that is, the set of initial conditions starting from where closed-loop stabilization under continuous availability of measurements is guaranteed), as well as the maximum allowable data loss rate which preserves closed-loop stability for the primary and the candidate backup configurations are computed. This characterization is utilized in identifying the occurrence of a destabilizing sensor fault, and in activating a suitable backup configuration that preserves closed-loop stability. The proposed method is illustrated using a chemical process example and demonstrated via application to a polyethylene reactor.
Model-based control and monitoring such as feed-forward/feedback control, fault detection and isolation (FDI), and fault-tolerant control (FTC) techniques that utilize Lyapunov-based control laws are implemented on a high recovery reverse osmosis desalination plant model. A detailed mathematical model of a high recovery reverse osmosis plant is developed. This model incorporates the large spatial variations of concentration and flow rate that occur in membrane units during high recovery operation. Bounded nonlinear feedback and feed-forward controllers are developed and applied to this system. The application of these controllers with FDI and FTC is demonstrated in the context of a high recovery reverse osmosis process simulation. The first set of simulations demonstrates the ability to compensate for the effects of large time-varying disturbances in the feed concentration on specific process outputs with and without feed-forward control. The second set of simulations demonstrates the ability of FDI and FTC techniques to recover desired plant operation subject to actuator failures.
This work addresses the problem of fault detection and isolation for nonlinear processes when some process variable measurements are available at regular sampling intervals and the remaining process variables are measured at an asynchronous rate. First, a fault detection and isolation (FDI) scheme that employs modelbased techniques is proposed that allows for the isolation of faults. The proposed FDI scheme provides detection and isolation of any fault that enters into the differential equation of only synchronously measured states and grouping of faults that enter into the differential equation of any asynchronously measured state. For a fully coupled process system, fault detection occurs shortly after a fault takes place, and fault isolation, limited by the arrival of asynchronous measurements, occurs when asynchronous measurements become available. Faulttolerant control methods with a supervisory control component are then employed to achieve stability in the presence of actuator failures using control system reconfiguration. Numerical simulations of a polyethylene reactor are performed to demonstrate the applicability and performance of the proposed fault detection and isolation and fault-tolerant control method in the presence of asynchronous measurements.
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