Distributed Model Predictive Control for Platooning of Heterogeneous Vehicles with Multiple Constraints and Communication Delays
Bibliographic record
Abstract
In this paper, the vehicle platoon control problems for a group of heterogeneous vehicles are investigated, where the multiple constraints of the vehicles and the communication delays among the vehicles are taken into consideration. A distributed model predictive control (DMPC) scheme is proposed to drive the heterogeneous vehicles into the desired platoon. In this DMPC framework, the multiple constraints, including the control constraints, state constraints, and jerk constraints, are employed to describe the practical characteristics of vehicles and the communication delays are time-varying and bounded. In this framework, a group of platoon control schemes is proposed based on the DMPC techniques. Furthermore, the feasibility and stability of the proposed vehicle platoon control system are strictly analyzed. Finally, numerical simulation and experiment with TurtleBot3 mobile robots are provided to validate the effectiveness of proposed approaches.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".