A big data-driven predictive control approach for nonlinear processes using behaviour clusters
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Bibliographic record
Abstract
A novel big data-driven predictive control (BDPC) approach for nonlinear processes is proposed. To deal with nonlinear process behaviours , the process behaviour space, represented by a set of input–output variable trajectories, is partitioned into linear sub-behaviour spaces (clusters), based on linear inclusion of nonlinear behaviours . A behaviour space (represented using Hankel matrices) partitioning approach is developed based on subspace angles. During online control, the BDPC controller locates the most relevant linear sub-behaviour based on the current online trajectory, which is then used to determine predictive control actions using receding horizon optimisation. The incremental stability and dissipativity conditions are developed to attenuate the effect of the error of approximating linear sub-behaviours on the output and guarantee closed-loop stability. These conditions are implemented as additional constraints during online data-driven predictive control. An example of controlling the Hall–Héroult process is used to illustrate the proposed approach.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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