Integrated Torque Vectoring Control for a Three-Axle Electric Bus Based on Holistic Cornering Control Method
Bibliographic record
Abstract
An integrated chassis control framework that consists of a basic chassis controller and a torque vectoring controller is designed for a three-axle electric bus with distributed motor-driven and active rear steering subsystems. In the basic chassis controller, the active speed limiting control is integrated for antisideslip and antirollover purposes, and the interaxle torque distribution ratio is optimized for energy economy. Meanwhile, the active rear steering control is designed for the tire-wear coordinating purpose. In the torque vectoring controller, the model-based motion control algorithm based on the holistic cornering control method is designed, by which a torque increment is generated at each wheel to change the plane motion states of the vehicle. To solve the optimal torque increment vector, a real-time constrained quadratic programming problem is formulated. The constraints related to the wheel torque limits, the tire friction limits, and the anti-wheel-slip requirements are constructed and converted as the upper and lower bounds of the increments of the longitudinal tire forces. To verify the performance of the control framework, a Trucksim-Simulink cooperative test platform is established. The test results show satisfactory performances on energy economy, anti-wheel-slip, and the safety and stability of the motion control.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".