A Comparative Study of Active Control Strategies for Improving Lateral Stability of Car-Trailer Systems
Bibliographic record
Abstract
This paper examines the performance of different active control strategies for improving lateral stability of car-trailer systems using numerical simulations. For car-trailer systems, three typical unstable motion modes, including trailer swing, jack-knifing and roll-over, have been identified. These unstable motion modes represent potentially hazardous situations. The effects of passive mechanical vehicle parameters on the stability of car-trailer systems have been well addressed. For a given car-trailer system, some of these passive parameters, e.g., the center of gravity of the trailer, are greatly varied under different operating conditions. Thus, lateral stability cannot be guaranteed by selecting a specific passive parameter set. To address this problem, various active control techniques have been proposed to improve handling and stability of car-trailer systems. Feasible control methods involve active trailer steering control (ATSC) and active trailer braking (ATB). Recently, a variable geometry approach (VGA) has been investigated. The essence of this method is to actively control the lateral displacement of the car-trailer hitch in order to improve high-speed stability of the vehicle system. To derive the three controllers, their respective yaw plane models are introduced. The simulation results based on each control method are examined and compared against each other. Through the benchmark comparisons, the features of different control strategies are identified and their applicability discussed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".