Development of Active Rear Axles Steering Controller For 8X8 Combat Vehicle
Bibliographic record
Abstract
Lateral dynamic control considered to be crucial to enhance the handling characteristics and stabilization of a vehicle as a safety demand. In this paper, active rear axles steering control system will be developed using an optimal quadratic regulator (LQR) control methodology. The controller aims to minimize the vehicle side slip and consequently increase its handling stability and transient state performance. The controller design has been utilized the independent steering of the vehicle’s 3rd and 4th axles as control inputs. Furthermore, the developed controller will be combined with a feedforward zero side slip (ZSS) controller based on the steady-state model of the vehicle and satisfying the Ackermann steering condition. In addition, the steady-state handling performance will be evaluated using the Skid Pad test. The transient state performance will be assessed at low Coefficient of Friction (CoF) surface using FMVSS 126 Electronic Stability Control (ESC) system test speed, while Open Loop Step Slalom Test will be used for assessing the controller at high CoF. The controllers will be implemented using MATLAB Simulink and will be simulated in a co-software simulation environment with Truck- Sim software. The results show a notable improvement in the steady and transient states handling performance in comparison with the Conventional, where the 3rd and 4th axles are fixed, and active 4th axle vehicle, where the 3rd axle is fixed. In addition, the controller succeeded to prevent the vehicle rollover and maintain a stable trajectory.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".