(654c) Fault-Tolerant Output Feedback Control of Multivariable Nonlinear Processes

Authors: 
Davis, J. F., University of California - Los Angeles
Gani, A., University of California, Los Angeles
McFall, C., University of California, Los Angeles
Mhaskar, P., McMaster University


The operation of chemical processes is characterized by the complexity of the individual units together with an intricate interconnection of these geographically distributed units via a network of material and energy streams, and control loops. The nonlinear behavior exhibited by most chemical processes, together with the presence of constraints on the operating conditions (dictated by performance considerations or due to limited capacity of control actuators), the presence of modeling uncertainty and disturbances, and the unavailability of all the process states as measurements has motivated several research results in the area of nonlinear control focusing on these issues.

The development of advanced control algorithms (alongside development in sensing, communicating and computing technologies) has lead to extensive automation of plant operation, resulting in the ability to satisfy simultaneously (the sometimes conflicting) requirements of safety, reliability and profitability. Increased automation, however, also makes the plant susceptible to faults (e.g., defects/malfunctions in process equipment, sensors and actuators, failures in the controllers or in the control loops), which can be compounded through the interconnection of the processing units leading to a failure of the entire control network which, if not appropriately handled in the control system design, can potentially cause a host of undesired economic, environmental, and safety problems that seriously degrade the operating efficiency of the plant. The above considerations provide a strong motivation for the development of methods and strategies for the design of advanced fault-tolerant controllers that account for process complexities such as nonlinearity, uncertainty and constraints and provide a mechanism for an efficient and timely response to enhance fault recovery. In a previous work [1], we considered the problem of fault-tolerant control of nonlinear systems with a single manipulated input and devised a strategy that achieves both fault-detection and controller reconfiguration to maintain closed-loop stability in the presence of faults.

This work considers the problem of developing advanced fault-tolerant control structures for multi-input multi-output nonlinear systems subject to multiple faults in the control actuators and constraints on the manipulated inputs. We design output-feedback fault-detection and isolation filters and output-feedback controllers via a combination of state-feedback fault-detection and isolation filters and controllers, and state estimators. The fault-detection and isolation filters essentially capture the difference between fault-free evolution of the system and the true evolution of the system to detect and isolate faults in the control actuators; precise conditions under which such fault isolation is possible are also derived. The state estimates are used in devising the reconfiguration rule that determines which of the backup control configurations should be implemented in the closed--loop system. Specifically, a configuration is chosen that 1) does not use the failed control actuator, and 2) guarantees stability of the closed--loop system starting from the system state at the time of the failure (this is ascertained via the use of feedback controllers that provides an explicit characterization of the output-feedback stability region). The implementation of the fault-detection and isolation filters and reconfiguration strategy is demonstrated on a chemical reactor network example and the robustness of the proposed method against measurement noise and modeling error is demonstrated through simulations.

[1]. Mhaskar, P., A. Gani, N. H. El-Farra, C. McFall, P. D. Christofides and J. F. Davis, "Integrated Fault Detection and Fault-Tolerant Control of Process Systems," AIChE J., 52, 2129-2148, 2006.