Agent-Based Model Predictive Controller (AMPC) for Flexible and Efficient Vehicular Control
Bibliographic record
Abstract
The rapid development of advanced vehicular mechatronic systems help to bring vehicle performances to a higher level. To fully unleash the power of such systems, an integrated vehicular control framework has been widely adopted. All actuators, along with their impact on vehicle dynamics are considered in a holistic model, and control performances based on such models are highly optimized thanks to a centralized distribution process. However, the strong reliance on a complex model with details for all subsystems limits the scalability and re-usability of resultant controllers. Any changes from vehicle topology to actuator details will lead to a controller redesign. In some cases, execution logic of actuators might even be proprietary and cannot be incorporated into a centralized coordination scheme. A flat architecture is proposed in our previous research to address such limitations, and a distributed multi-agent control scheme is suggested for a specialized modular vehicle platform. This paper generalizes such approach, and focuses on addressing model scalability and re-usability in complex vehicular active safety applications. When properly formulated, the proposed agent-based cooperative controllers are shown to give no compromise to control performances as their integrated-designed counterparts. The proposed framework also features greater flexibility in handling vehicle configuration changes as well as actuator modelling granularities. Incorporation of proprietary subsystems as “black-box” agent modules expands the coverage of the proposed architecture for advanced vehicular mechatronic systems.
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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 | 0.001 | 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) | 0.002 | 0.001 |
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".