Multi-level data-predictive control for linear multi-timescale processes with stability guarantee
Why this work is in the frame
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Bibliographic record
Abstract
Multi-timescale dynamics are common in chemical processes. These processes are often difficult to model and pose challenges in control system design. In this paper, we propose a data-based control approach for linear multi-timescale systems using a system behavioural framework. A data resampling method coupled with a novel data predictive control (DPC) design with multi-level optimisation horizons is developed to handle different timescales. To deal with the dynamics of different timescales, the optimisation horizons with small to large time intervals are used to predict and optimise control actions from near to distant future. Computational complexity wise, the multi-level structure allows horizon length to expand exponentially with optimisation steps. A trajectory-based dissipativity condition is also developed to ensure stability of the proposed DPC, while achieving disturbance rejection and tracking control. An example of controlling a multi-timescale reactive distillation column is presented 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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