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Coventional control schemes are developed under the assumption that the sensors and actuators are free from faults. However, the occurrence of faults will cause degradation in the closed-loop performance and also will have an impact on safety, productivity, and plant economy. In the present work, we have proposed a fault-tolerant control scheme (FTCS) by integrating a fault detection and identification (FDI) technique with conventional control. The principal component of our proposed FTCS is a compensation strategy (supervisory system) which uses the information provided by the FDI to appropriately modify the controller as well as the model used in FDI. This allows online application of the FTCS without causing significant degradation in the closed-loop performance due to the occurrences of biases in sensors and actuators or due to changes in unmeasured disturbance variables and due to moderate change in process parameters. Through stochastic simulation studies of a continuous stirred tank reactor process, we demonstrate that our proposed FTCS leads to significant improvement in the closed-loop performance in comparison to a conventional control scheme, especially as the fault magnitude increases. The proposed compensation scheme also allows identification of multiple faults that occur sequentially in time and is also found to be robust with respect to moderate plant−model mismatch.

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This article presents a first principles simulation of a continuous stirred tank heater pilot plant at the University of Alberta. The model has heat and volumetric balances, and a very realistic feature is that instrument, actuator and process non-linearities have been carefully measured, for instance to take account of the volume occupied by heating coils in the tank. Experimental data from step testing and recordings of real disturbances are presented. The model in Simulink and the experimental data are available electronically, and some suggestions are given for their application in education, system identification, fault detection and diagnosis.

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