An independently controllable active steering system for maximizing the handling performance limits of road vehicles
Bibliographic record
Abstract
This study explores the effectiveness of an active independent front steering system capable of applying a corrective steering at each wheel selectively and in an independent manner. In doing this, it is possible to generate the required adhesion force for active steering control while ensuring that none of the tyres approaches saturation. A non-linear yaw-plane model of a two-axle truck with a limited number of roll degrees of freedom is used to evaluate the effectiveness of the active independent front steering under a range of steering manoeuvres. A simple proportional–integral controller is synthesized to track the reference response based on the neutral steering system as well as to limit the steering correction considering the saturation limit of the tyres, which is defined from the normalized cornering stiffness properties of the tyres. The directional responses obtained for the vehicle model integrating the active independent front steering controller are compared with those of the conventional active front steering system for each of the selected manoeuvres. The results show that, while both the control strategies can effectively track the target yaw rate of the vehicle, the proposed active independent front steering control can yield enhanced performance limits without any of the tyres approaching the saturation limit, irrespective of the road condition and the steering manoeuvre. Furthermore, the active independent front steering design permits some adhesion reserve for each wheel for generating additional traction and braking forces during a severe steering manoeuvre.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".