Dynamics Analysis of Car-Trailer Systems with Active Trailer Differential Braking Strategies
Bibliographic record
Abstract
To date, various control strategies based on linear vehicle models have been researched and developed for improving lateral stability of car-trailer (CT) systems. Is a linear-model-based controller applicable to active safety systems for CT systems under emergency operating conditions, such as an evasive maneuver at high lateral accelerations? In order to answer the question, the applicability of an active trailer differential braking (ATDB) controller designed using a linear CT model is tested and evaluated, while the controller being applied to a CT system represented by a linear and a nonlinear CT model. The current research leads to the following insightful findings: the ATDB controller designed using the linear model can effectively improve the lateral stability of CT systems under regular evasive maneuvers at low lateral accelerations, but the controller is not applicable to CT active safety systems under emergency evasive maneuvers at high lateral accelerations. The insightful findings resulted from the paper will provide valuable design guidelines for the development of active safety systems for CT 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".