Robust Tuning of Machine Directional Predictive Control of Paper Machines
Bibliographic record
Abstract
In this work, a parameter tuning problem of two-degrees-of-freedom model predictive control of industrial paper-making processes is explored to achieve satisfactory time-domain robust closed-loop performance in terms of worst-case overshoots and worst-case settling times, under user-specified parametric uncertainties. An efficient visualization method is first developed to characterize the set of time-domain closed-loop responses in the presence of parametric model–plant mismatch. On the basis of the visualization technique and the unmodality/monotonicity properties of the performance indices with respect to the tuning parameters, the feasibility of the tuning problem can be analyzed, and a three-step iterative line-search based automatic tuning algorithm is proposed to determine the controller parameters that meet the time-domain performance requirements robustly for the given parametric uncertainty specifications. The effectiveness of the algorithm is illustrated by applying the results to a process from stock to conditioned weight in an industrial paper machine and by comparing the performance of the algorithm with that of brutal search.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".