(564f) Fault-Tolerant Economic Model Predictive Control with Empirical Process Models
AIChE Annual Meeting
Wednesday, November 1, 2017 - 2:05pm to 2:24pm
In this work, we develop an approach for accounting for actuator faults in MPC based on empirical models by utilizing an error-triggering procedure to initiate on-line updates of the empirical model based on significant prediction error due to the fault, assuming that it is known which actuator has experienced a fault. The methodology presented is developed in the context of economic model predictive control (EMPC) with empirical models  due to the potential of this control design to dictate time-varying process operation by computing a time-varying input trajectory, which may cause it to compute inputs that persistently excite the process dynamics so that routine process operating data can be utilized for the on-line model identification procedure. Specifically, the implementation strategy of the EMPC is updated after a fault to either incorporate the value of the faulty actuator output within the empirical model (reducing the number of decision variables) when the value at which the faulty actuator output is fixed is known, or, when the value of the faulty actuator output is not known, the EMPC continues to solve for all inputs but only implements those for the non-faulty actuators. A moving horizon error detector computes the relative prediction error in all process outputs throughout a prediction horizon and initiates model re-identification when this error exceeds a pre-specified threshold , where the new model identified after a fault has one less input since the faulty actuator output is no longer available to be adjusted by the EMPC. A chemical process example demonstrates the effectiveness of the proposed strategy at compensating for the actuator fault compared to utilizing a single empirical model throughout the time of operation despite the faults. It also demonstrates that the moving horizon error detector is effective at determining the necessity of updating the empirical model after a fault, so that the model is only changed within the control design when significant prediction error is detected.
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