Integrated model predictive and torque vectoring control for path tracking of 4‐wheel‐driven autonomous vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this study, an integrated path tracking control framework is proposed for the independent‐driven autonomous electric vehicles. The proposed control scheme includes three parts: the non‐linear model predictive path tracking controller, the lateral stability controller, and the optimal torque vectoring controller. Firstly, the upper bound speed limit is regulated based on the known curvature and adhesion coefficient of the road to prevent the tyre saturation. The model predictive controller generates the steering angle and the desired longitudinal force for path tracking. Simultaneously, the lateral stability controller calculates the desired yaw moment to balance the vehicle stability and motility under different situations. Finally, the optimal torque vectoring controller distributes the wheel torques to generate the desired longitudinal force and yaw moment. Three test cases are designed and verified based on a Carsim/Simulink platform to evaluate the control performance. The test results illustrate that the proposed control framework has satisfactory path tracking performance, and the desired balance between vehicle mobility and stability is achieved under different road conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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