Active Safety Control of Automated Electric Vehicles at Driving Limits: A Tube-Based MPC Approach
Bibliographic record
Abstract
To enhance the active safety performance for automated electric vehicles (AEVs) at driving limits, the collaborative control of four-wheel steering (4WS) and direct yaw-moment control (DYC) is adopted. To deal with external disturbance and modeling error, tube-based model predictive control (MPC) is applied to the control algorithm design, which takes the improvement of handling stability and path-tracking performance into considerations. Taking the constraints into account, including control vector constraints, lateral stability constraints, rollover prevention constraints, and path-tracking error constraints, the integrated controller is designed and worked out by addressing the optimization issue. To verify the effectiveness and feasibility of the integrated controller, two extreme driving conditions are conducted based on hardware-in-the-loop (HIL) tests. The test results indicate that the integrated controller can improve vehicle’s handling stability and path-tracking performance in unison at driving limits. Besides, the integrated controller shows strong robustness in extreme conditions.
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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 | 0.001 | 0.001 |
| 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".