Performance Enhancement of Road Vehicles Using Active Independent Front Steering (AIFS)
Bibliographic record
Abstract
Technological developments in road vehicles over the last two decades have received considerable attention towards pushing the safe performance limits to their ultimate levels. Towards this goal, Active Front Steering (AFS) and Direct Yaw-moment Control (DYC) systems have been widely investigated. AFS systems introduce corrective steering angles to conventional system in order to realize target handling response for a given speed and steering input. It is thus expected that such an action under severe maneuvers may cause one tire to reach saturation while the other tire may be capable of developing more force. This study, therefore, proposes an Active Independent Front Steering (AIFS) system capable of controlling a wheel independently. At low speeds, the proposed AIFS system will modify the steer angle with speeds while maintaining pro-ackerman geometry similar to an AFS system. In doing so, it will realize a target response defined as one provided by a neutral steer system. However, in a severe maneuver, as the inner tire approaches the saturation limit, the AIFS system controller will only increase the angle of the outer tire, effectively introducing an anti-ackerman geometry. The study is carried out using a comprehensive 4-wheel handling model with AIFS capability. A PI controller with ability to detect and control the outer wheel independently is incorporated to examine the handling performances of an understeer vehicle under a ramp-step and sinusoidal steering inputs. In general, the results demonstrate that AIFS can perform as well as AFS in realizing target response while AIFS can provide performance enhancement beyond the limits of AFS. The control approach of AIFS is also shown to be effective for split-μ condition. Language: en
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".