Coordinated-distributed MPC of nonlinear systems based on price-driven coordination
Why this work is in the frame
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Bibliographic record
Abstract
The main objective of this work is to design a coordinated distributed model predictive control (CDMPC) architecture for nonlinear systems using the price-driven coordination method. One fundamental assumption of this work is that a centralized model predictive control (MPC) scheme can be designed based on successive linearization of the nonlinear system that is able to asymptotically stabilize the closed-loop system at the origin and the coordination schemes strives to get to the very same performance via adding a coordinator level to the existing decentralized structure. In other words, the coordinator and the distributed MPCs exchange information and calculate the optimal future input trajectories iteratively. In order to deploy the price-driven coordination algorithm developed by Marcos [1], which is applicable to linear or linearized systems, the alkylation process of benzene used as a case study was successively linearized around the operating points. The simulation results demonstrate that the algorithm can be successfully applied to nonlinear systems using a successive linearization strategy.
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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