Direct Yaw Control Based on Optimal Longitudinal Tire Forces for 8×8 Combat Vehicle
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">This paper proposes an active chassis control strategy for an Eight-wheel drive/Four-wheel steering (8WD/4WS) combat vehicle, where only the first and second axles’ wheels are steerable, while the third and fourth axles’ wheels are non-steerable. Utilizing torque vectoring and differential braking control to improve its lateral dynamics at limit handling. Due to the non-linear characteristics of the tires and its friction limit, the vehicle may exhibit instable behavior during cornering maneuvers. It is well known that the tire longitudinal and lateral forces are shared, if longitudinal forces increased, slip ratio will increase and causing reduction in lateral forces that may cause the vehicle to drift out or spinning. Accordingly, the tires forces need to be optimally distributed based on vertical loads for each tire to prevent it from reaching the friction limit based on Friction Ellipse Theorem. In order to enhance vehicle maneuverability and stability under different road conditions, a hierarchical control strategy is implemented. The proposed control strategy consists of an upper and lower level controller. The upper level controller is designed to generate the desired corrective yaw moment based on vehicle parameters and reference model. While the lower controller is designed on two stages for Torque Vectoring and three stages for differential braking. For torque vectoring, the first stage is designed to generate the optimal tractive forces needed to stabilize the vehicle based on the friction circle (work load) of each tire for each given axle at a given tire vertical load, while the second stage includes an on/off slip controller for each tire to prevent exceeding the slip peak ratio. The differential braking control allocation incorporates braking wheels coordination as a first stage to determine which wheels braking torque should be engaged. Optimal braking forces distribution are implemented as a second stage, subsequently an on/off slip controller is introduced as third stage. Furthermore, all simulation tests are conducted in TruckSim and Simulink environment at limit handling conditions at different coefficient of friction for Double Lane Change (DLC) and Slalom maneuvers.</div></div>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".