Wheel Modules With Distributed Controllers: A Multi-Agent Approach to Vehicular Control
Bibliographic record
Abstract
Modular vehicles have great potential to solve future mobility challenges in an efficient, reliable, and flexible way. Various designs have been proposed for compact, and multi-functional wheel modules. However, the architecture of vehicular active safety systems, by adopting such a modular approach, has not yet been well studied in the literature. Conventional stability control systems are developed in a centralized manner to seek the optimal distribution of control efforts, and the resultant controllers are highly dependent on the vehicle platform. Any changes to the actuators as well as modelling complexity will lead to a system redesign. This paper proposes a distributed control architecture as inspired by the modular chassis design for great re-usability. Each wheel module is treated as an agent in a cooperative game to determine their share in the overall control efforts, while encapsulating their configuration as well as modelling details within each module. Re-configurable agent models are also derived in a weak-coupled fashion to enhance modularity of the control system. The resultant distributed model predictive controller demonstrates greater flexibility in adaption to various configurations as well as enhanced robustness against computational units failure in the system. The proposed architecture also offers a new perspective for the controller design of conventional vehicles.
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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 | 0.000 |
| 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 | 0.001 |
| 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".