Distributed model predictive control with asynchronous controller evaluations
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In this work, we focus on the reduction of network communication burden of cooperative distributed model predictive control (DMPC) of a class of nonlinear processes. Specifically, we propose a cooperative DMPC design in which the evaluations of the distributed controllers are triggered by the difference between the subsystem state measurements and the estimates of them. The individual model predictive controllers in this DMPC are designed via Lyapunov techniques. Under the assumption that state measurements of the subsystems are available, sufficient conditions for the closed‐loop stability are derived. The proposed DMPC is applied to a reactor–separator chemical process example and is compared with a cooperative DMPC in which distributed controllers are evaluated every sampling time extensively. The results demonstrate the applicability and effectiveness of 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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