Handling Plant Variation via Error-Triggered On-line Model Identification: Application to Economic Model Predictive Control

Abstract: In the present work, an error-triggered on-line model identification approach is introduced for closed-loop systems that is able to detect and compensate for significant disturbances and variations in the plant. A moving horizon error detector is used to quantify the model accuracy and to trigger the model re-identification on-line when necessary. The proposed approach is applied in the context of economic model predictive control (EMPC), which is a feedback control approach that optimizes plant economics on-line by utilizing a process model. The on-line model identification scheme coupled with the EMPC system was applied to a benchmark catalytic chemical reactor example where the reaction rates decrease with time due to catalyst deactivation. In the presence of such plant variation, more accurate state predictions were made under the proposed EMPC scheme with on-line model identification than under an EMPC for which the model was not updated, which significantly impacted the plant economics.

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