Fourth Axle Steering Control of an 8x8 Scaled Electric Combat Vehicle
Bibliographic record
Abstract
With the rise in demand, advanced steering control and electric vehicle technology are rapidly developing in modern times. Due to a controller's role as a backbone for the modern vehicle, its study has become increasingly crucial. This research proposes a novel 4th axle steering (4AS) feedforward controller that utilizes the first, second and fourth axle steering control for an 8x8 scaled electric combat vehicle. The vehicle is tested using the predefined path following. The novel 4AS controller is then compared to the Ackermann steering condition at different speeds. In the scaled vehicle used for this research, each wheel is independently driven by an in-wheel motor, while the steering is carried out by linear actuators. Individual eight-wheel steering control systems are designed and installed on the scaled vehicle to evaluate the driving performance from low speed to high speed. The 4AS steering method is implemented to improve the stability of the scaled vehicle at high speeds. The rear steering control strategy with Ackermann steering shows better driving performance at low-speed tests, but the active 4th axle steering controller represents more vehicle dynamic stability at high-speed maneuvers. This study uses MATLAB/ Simulink software to build and implement the controllers, and TruckSim modelling and simulation software to simulate the on-road conditions for a given maneuver. The simulation results, which include eight-wheel steering angles, trajectory, and vehicle sideslip are advantageous to the design of full-size 8x8 electric combat vehicles. A successful outcome of this paper would be to compare the results to determine the better method of steering at different speeds for the full-size combat vehicle.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".