Closed-loop subspace projection based state-space model-plant mismatch detection and isolation for MIMO MPC performance monitoring
Why this work is in the frame
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Bibliographic record
Abstract
In multivariate model predictive control (MPC) systems, the quality of multi-input multi-output (MIMO) plant models has significant impact on the controller performance in different aspects. Though re-identification of plant models can improve model quality and prediction accuracy, it is very time consuming and economically expensive in industrial practice. Therefore, the automatic detection and isolation of the model-plant mismatch is highly desirable to monitor and improve MPC performance. In this paper, a new closed-loop MPC performance monitoring approach is proposed to detect model-plant mismatch within state-space formulations through subspace projections and statistical hypothesis testing. A monitoring framework consisting of three quadratic indices is developed to capture model-plant mismatches precisely. The validity and effectiveness of the proposed method is demonstrated through a paper machine headbox control example.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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