Assessing Model Prediction Control (MPC) Performance. 2. Bayesian Approach for Constraint Tuning
Bibliographic record
Abstract
Performance assessment of model predictive control (MPC) systems has been focusing on evaluation of the variability with, for example, minimum variance or LQG/MPC tradeoff curve as benchmarks. These previous studies are mainly concerned with the dynamic performance of MPC. However, the benefit of MPC is largely attributed 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 MPC performance by considering economic performance, variability reduction, and their relationships. One of the good indications of this relation is the constraint tuning. In practical MPC applications, the constraint setups are important whenever an MPC is commissioned, and constraint tunings are not uncommon, even when the MPC is already on-line. Thus, the questions to ask are which constraints should be adjusted, and what is the benefit to do so? By investigating the relationship between variability and constraints, problems of interest are solved under the Bayesian inference framework (namely, through the Bayesian approach for decision evaluation and decision-making). The decisions that are referenced are whether to tune the constraints to achieve the optimal economic MPC performance and which constraints should be tuned. A detailed case study for a distillation column MPC application is provided to illustrate the proposed performance assessment methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".