Vehicle dynamics control using an active third-axle system
Bibliographic record
Abstract
This paper introduces the active third-axle system as an innovative vehicle dynamic control method. This method can be applicable for different kinds of three-axle vehicles such as buses, trucks, or even three-axle passenger cars. In this system, an actuator on the middle axle actively applies an independent force on the suspension to improve the handling characteristics, and hence, its technology is similar to slow-active suspension systems. This system can change the inherent vehicle dynamic characteristics, such as under/over steering behaviour, in the linear handling region, as well as vehicle stability in the nonlinear, limit handling region. In this paper, our main focus is to show the potential capabilities of this method in enhancing vehicle dynamic performance. For this purpose, as the first step, the proposed method in both linear and nonlinear vehicle handling regions is studied mathematically. Next, a comprehensive, nonlinear, 10 degrees of freedom vehicle model with a fuzzy control strategy is used to evaluate the effectiveness of this system. The dynamic behaviour of a vehicle, when either uncontrolled or equipped with the active third axle is then compared. Simulation results show that this active system can be considered as an innovative method for vehicle dynamic control.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".