Distributed Model Predictive Control of Nonlinear Systems Based on Price-Driven Coordination
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".