Distributed Model Predictive Control of Nonlinear Systems Based on Price-Driven Coordination
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Bibliographic record
Abstract
Here, a nonlinear plant is considered, which is operated by a decentralized control system. The existing system ignores the interactions between subsystems, which often results in uncaptured plantwide performance. The focus of this paper is on the design of a distributed model predictive control (DMPC) network using successively linearized internal models. In this method, all existing interactions between the subsystems should be considered in order to enhance the performance of the current decentralized DMPC scheme. A coordination layer is added to the existing network, while minor modifications are applied to the local MPC controllers, to achieve the performance and stability of a hypothetical centralized MPC for the entire plant. In this work, an interior-point algorithm is proposed to coordinate a DMPC network via the price-driven coordination approach. In addition, the convergence of the algorithm is shown, and the necessary conditions to ensure the closed-loop stability of the system are provided for the situation when the algorithm is terminated prematurely prior to convergence.
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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 | 0.001 |
| 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 | 0.001 |
| 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