Study on lateral stability of distributed drive electric tractor semi-trailers under low adhesion road conditions
Bibliographic record
Abstract
To improve the lateral stability of tractor-semitrailer under low adhesion road surface, a direct yaw torque control strategy considering longitudinal speed control was proposed based on distributed electric drive technology. A hierarchical control strategy was designed, with the upper layer using a linear quadratic regulator (LQR) controller to calculate the yaw moment. To distribute the longitudinal force and additional yaw moment, the torque distribution method was adopted based on quadratic programming in the lower controller. The co-simulation was conducted by Trucksim and Matlab/Simulink under the conditions of single lane change (SLC) of 110 km/h and double lane change (DLC) of 70 km/h. The results indicate that in comparison with the PID controller and the uncontrolled vehicle, the LQR controller improves the lateral stability of the vehicle on the low-adhesion road.
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How this classification was reachedexpand
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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".