Coordinated control for path-following of an autonomous four in-wheel motor drive electric vehicle
Why this work is in the frame
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Bibliographic record
Abstract
Coordination of Active Front Steering (AFS) and Direct Yaw Moment Control (DYC) has been widely used for non-autonomous vehicle lateral stability control. Recently, some researchers used it (AFS/DYC) for path-following of autonomous vehicles. However, current controllers are not robust enough with respect to uncertainties and different road conditions to guarantee lateral stability of Autonomous Four In-wheel Motor Drive Electric Vehicles. Thus, a coordinated control is proposed to address this issue. In this paper, a two-layer hierarchical control strategy is utilized. In the upper-layer, a self-tunable super-twisting sliding mode control is utilized to deal with parametric uncertainties, and a Model Predictive Control (MPC) is used in order to allocate the control action to each AFS and DYC. Parametric uncertainties of tires' cornering stiffness, vehicle mass and moment of inertia are considered. Simulations with different road conditions for path-following scenario have been conducted in MATLAB/Simulink. An autonomous vehicle equipped with Four In-wheel Motor and two degrees of freedom vehicle dynamics model is used in this study. In the end, the performance of the proposed controller is compared with the MPC controller. Simulation results reveal that the proposed controller provides better path-following in comparison with the MPC controller.
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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 | 0.003 | 0.001 |
| 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 | 0.001 | 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