A Zonotope-based Big Data-driven Predictive Control Approach for Nonlinear Processes
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Bibliographic record
Abstract
A zonotope-based big data-driven predictive control (BDPC) approach is developed to partition the nonlinear process behaviour (represented by an input-output trajectory set) into multiple linear sub-behaviours using a two-step hierarchical clustering: Euclidean distance-based clustering and linear subspace distance-based clustering. By approximating every linear sub-behaviour as a zonotope, a data-driven interpolation is developed based on the convex combination of zonotopes. During online control, a BDPC controller is designed by determining an interpolated zonotope where its centre trajectory is closest to the online trajectory and computing the control action subject to an optimisation problem. The proposed BDPC approach is illustrated using a case study on controlling an aluminium smelting process.
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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 | 0.000 |
| Open science | 0.001 | 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