Big data-driven predictive control for nonlinear systems—A trajectory cluster-based contraction approach
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Bibliographic record
Abstract
This article presents a novel contraction-based big data-driven predictive control (CBDPC) approach for nonlinear systems using the behavioural systems framework. The nonlinear behavioural space is partitioned into linear sub-behavioural spaces, represented by connected trajectory clusters. The controller drives the process to travel through multiple linear sub-behavioural spaces to reach the setpoint. By introducing the concepts of data-based contraction and differential dissipativity, a trajectory cluster-based control contraction metric and contraction condition are developed to guarantee incremental exponential stability of the controlled nonlinear system behaviour and attenuate the effect of linear sub-behaviour approximation errors on controlled output. Connected trajectory clusters are obtained via multi-view fuzzy clustering, which partitions nonlinear system behaviour (i.e., a set of input–output data trajectories) into connected linear sub-behaviours (i.e., trajectory subsets with intersections). Based on the above contraction and dissipativity conditions, an online data-driven predictive control approach using Hankel matrices is developed. The proposed approach is illustrated using a case study on control of an aluminium smelting process, which demonstrates the control performance achieved by the CBDPC 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 | 0.001 | 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