Abstract
In this work, a new approach to integrate Model Predictive Control (MPC) and Real-Time Optimization (RTO) layers within an hierarchic control structure is presented. When compared to the standard hierarchic structure, the proposed approach improves the tracking of the optimum operating point and minimizes inconsistencies that might occur due to different calculations made in each layer, each one based on a different process model. The strategy consists of the introduction of a sub-layer steady-state target optimization (SSTO) into the MPC layer. This sub-layer will reevaluate the targets from the RTO to setpoints attainable by the MPC. Additionally, the SSTO–MPC augmented layer uses identification techniques to help keep its models continuously updated, improving predictions and mitigating unexpected behavior. The proposed approach is validated through simulated example and the obtained results are validated in a benchmark model also used in a similar work done on the Economic Model Predictive Control approach.
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This work is supported by IFSC and UTFPR through granted CAPES/DINTER.
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Pozas, L.F., de Arruda, L.V.R. A New Approach to Integrate SSTO, MPC and RTO Using Online Identified Models. J Control Autom Electr Syst 29, 566–575 (2018). https://doi.org/10.1007/s40313-018-0397-4
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DOI: https://doi.org/10.1007/s40313-018-0397-4