Automated Two-Degree-of-Freedom Model Predictive Control Tuning
Bibliographic record
Abstract
This work considers the automated tuning of a two-degree-of-freedom model predictive controller (MPC) for single-input, single-output industrial processes with model uncertainties. The objective of the tuning algorithm is to automatically determine the MPC tuning parameters such that (1) the robust stability can be guaranteed, (2) the worst-case overshoot is controlled, (3) the oscillations in process outputs are attenuated, and (4) the worst-case settling time is minimized. A rigorous robust stability analysis is first conducted based on the connection between parametric uncertainties and unstructured uncertainties, and a tight robust stability condition is derived. As the specification on process output variation is not easily made by the end users, two alternative methods are proposed to automatically determine the tolerable total variation, which lead to two automatic tuning algorithms that achieve the tuning objectives. The proposed results are tested and verified through examples extracted from industrial processes in the pulp and paper industry, and comparisons are made with other existing results.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".