A Non-Cooperative Distributed Model Predictive Control Using Laguerre Functions for Large-Scale Interconnected Systems
Bibliographic record
Abstract
This paper deals with a new non-cooperative distributed controller for linear large-scale systems based on designing multiple local Model Predictive Control (MPC) algorithms using Laguerre functions to enhance the global performance of the overall closed-loop system. In this distributed control scheme, that does not require a coordinator, local MPC algorithms might transmit and receive information from other sub-controllers by means of the communication network to perform their control decisions independently on each other. Thanks to the exchanged information, the sub-controllers have in this way the ability to work together in a collaborative manner towards achieving a good overall system performance. To decrease drastically the computational load in the small-size optimization problem with a short prediction horizon, discrete-time Laguerre functions are used to tightly approximate the optimal control sequence. For evaluating the proposed distribution control framework, a simulation example is proposed to show the effectiveness of the proposed scheme and its applicability for large-scale interconnected systems. The obtained simulation results are provided to demonstrate clearly that the proposed Non-Cooperative Distributed MPC (NC-DMPC) outperforms Decentralized MPC (De-MPC) and achieves performance comparable to centralized MPC with a reduced computing time. The system performance of the proposed distributed model predictive control is given.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".