| Abstract
| - Performance assessment of model predictive control (MPC) systems has been focusing on evaluation of thevariability with, for example, minimum variance or LQG/MPC tradeoff curve as benchmarks. These previousstudies are mainly concerned with the dynamic performance of MPC. However, the benefit of MPC is largelyattributed to its capability for economic optimization. The economic performance, on the other hand, is also dependent on the variability reduction achieved through dynamic control. There is a need to assess MPCperformance by considering economic performance, variability reduction, and their relationships. One of thegood indications of this relation is the constraint tuning. In practical MPC applications, the constraint setupsare important whenever an MPC is commissioned, and constraint tunings are not uncommon, even when theMPC is already on-line. Thus, the questions to ask are which constraints should be adjusted, and what is thebenefit to do so? By investigating the relationship between variability and constraints, problems of interestare solved under the Bayesian inference framework (namely, through the Bayesian approach for decisionevaluation and decision-making). The decisions that are referenced are whether to tune the constraints toachieve the optimal economic MPC performance and which constraints should be tuned. A detailed casestudy for a distillation column MPC application is provided to illustrate the proposed performance assessmentmethods.
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