Handling Stability Advancement With 4WS and DYC Coordinated Control: A Gain-Scheduled Robust Control Approach
Why this work is in the frame
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Bibliographic record
Abstract
This paper focuses on the stability control algorithm for four-wheel independent steering (4WIS) and four-wheel independent drive (4WID) electric vehicle (EV) with the coordinated control of four-wheel steering (4WS) and direct yaw-moment control (DYC) techniques. In order to design an adaptive gain-scheduled robust controller for stability control, linear parameter-varying (LPV) system and H∞ optimal control theory are applied. The polytopic model is proposed to build the LPV system for 4WIS-4WID EV. Taking structured uncertainties and sensor noise into consideration, gain-scheduled robust controller is designed and worked out using linear matrix inequality (LMI). To verify the performance of the adaptive gain-scheduled robust controller, fishhook maneuver and sinusoidal steering maneuver are carried out based on hardware-in-the-loop (HIL) tests. Test results indicate that the adaptive gain-scheduled robust controller can improve vehicle's handling stability especially in extreme conditions. Meanwhile, the designed controller shows strong robustness to suppress system parametric perturbation.
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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 | 0.001 |
| 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 it