Decentralized receding horizon control with communication bandwidth allocation for multiple vehicle systems
Bibliographic record
Abstract
SUMMARY In this paper, a decentralized receding horizon control (DRHC) for a group of cooperative vehicles is investigated where the communication bandwidth is limited. This gives rise to a DRHC problem with communication delays. A new approach is proposed to vary the communication bandwidth for each vehicle, subject to network bandwidth constraints, in order to improve the cooperation performance. In the DRHC approach, each vehicle predicts its future trajectory over a prediction horizon and the neighboring vehicles exchange their predicted trajectories at each sample time to maintain the cooperation objectives. A delayed DRHC architecture is formulated that explicitly accounts for the inter‐vehicle communication delays. Then a bandwidth allocation algorithm is proposed for the delayed DRHC formulation. The key idea with the proposed approach is that each vehicle minimizes an error bound due to the mismatch between the delayed and updated neighbor's trajectories. This allows a dynamic bandwidth allocation to optimize the group performance. Simulation of formation of a group of vehicles is used to demonstrate the effectiveness of the approach. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.002 |
| 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 | 0.001 | 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".