An improved dynamic matrix control with output penalty for industrial processes
Bibliographic record
Abstract
Abstract For plants with complex dynamics, traditional dynamic matrix control (DMC), which includes output errors and control increments in the cost function, usually exhibits limited control performance. To obtain a better performance in system overshoot, we propose a novel DMC method with an output penalty term without increasing the computational complexity. First, we design a quadratic term for output increments and integrate it into the cost function. This term is used to penalize the system output, thereby reducing system overshoot. Considering that the cost function already contains three performance indexes, we propose a fast DMC algorithm to reduce the computational burden of the controller. The main idea of the proposed fast DMC is to move part of the control computation offline to reduce the cost of online computation. Finally, the proposed DMC strategy is applied to the oxygen content system of the coke furnace. The results show that the proposed DMC method is superior to the conventional DMC method in terms of reducing system overshoot. In addition, the proposed method achieves a significant improvement in computational efficiency.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".