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An Automatic Tuning Method for Model Predictive Control Strategies
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DOI：10.1021/acs.iecr.9b03502.png)
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Abstract
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The proposed auto-tuning method is a layer of receding horizon optimization problem whose objective function is able to consider the performance/robustness through the trade-off representation combining the responses of the process variables and the control actions. The tuning parameters are evaluated based on a closed-loop simulation in the presence of model-plant mismatch, which is compared to the desired tunnel response in the time domain. The method is tested for two types of model predictive control, and its flexibility and suitability for different scenarios of fit criteria are presented. The results point to the feasibility of the applications of the method, keeping the system close to the ideal setting and highlighting the importance of evaluating the behavior of control actions in the tuning problem.
Keywords:
OPTIMIZATION
ALGORITHM
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