Automated Two-Degree-of-Freedom Model Predictive Control Tuning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it