Acceleration slip regulation control for four-wheel independently drive electric vehicle based on fuzzy control
Why this work is in the frame
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Bibliographic record
Abstract
To improve the acceleration performance and stability of the four-wheel independent drive (4WID) electric vehicle on low-adhesion road, a fuzzy control that doesn’t depend on accurate vehicle models is proposed. Taking the driving torque of one side wheel as a reference the slip rate is controlled by controlling the torque errors between the left and right wheels to a certain range. Carsim-Simulink co-simulation is used to analyze the acceleration stability of 4WID electric vehicle on low-adhesion road and μ-split road. The simulation results show that the wheel slip rate can be controlled within a reasonable range through proposed method, and the stability and safety of the vehicle can be effectively improved on the basis of ensuring the power performance of the vehicle.
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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) | 0.000 | 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 it