Development of control strategies of a multi-wheeled combat vehicle
Bibliographic record
Abstract
This work develops a vehicle dynamics controller for vehicle stability, manoeuvrability and turning circle reduction for an 8 × 8 heavy combat vehicle utilising both torque vectoring and third and fourth axle steering. The proposed control scheme is composed of two distinct controllers, each with their own range of operation based on vehicle speed. A feedforward zero side slip (ZSS) controller actuates the third and fourth axle steering angles. It is used for manoeuvring at speeds of 30 kph and below and for turning circle reduction. A two degrees of freedom (DOF) linear parameter varying (LPV) H∞ controller that monitors steering wheel angle and yaw rate error and uses both the rear axle steering and torque vectoring to enhance vehicle stability and manoeuvrability at speeds above 40 kph. Gaussian distribution functions are used to switch from one controller to the other. The proposed control scheme is evaluated by running simulations using a validated computer simulation (TruckSim) full vehicle model in co-simulation with the controller and developed electric powertrain in Simulink. The proposed control system is able to greatly improve vehicle stability and manoeuvrability. A turning circle reduction of 30% is obtained using the ZSS feedforward method.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".