An Automated Design Method for Active Trailer Steering Systems of Articulated Heavy Vehicles
Bibliographic record
Abstract
An important design decision for active trailer steering (ATS) systems for articulated heavy vehicles (AHVs) is the trade-off between maneuverability and lateral stability. This paper presents an automated design method for this trade-off. The proposed method has the following features: (1) a design framework for bilevel optimization of ATS systems is formulated; (2) design variables of ATS controllers and trailers are optimized simultaneously; (3) two controllers are designed for the ATS system for improving stability and enhancing maneuverability, respectively; and (4) a driver model is introduced in the virtual vehicle simulation for closed-loop testing maneuvers. The design framework allows automation of vehicle modeling, controller construction, performance evaluation, and design variable selection, and all required design processes are implemented in a single loop. The proposed method is compared to a previously published two-loop design method, showing that the new approach can effectively identify desired variables and predict performance envelopes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".