A coupled force predictive control of vehicle stability using front/rear torque allocation with experimental verification
Bibliographic record
Abstract
This paper investigates the handling control and stability of an all-wheel-drive vehicle whose axles are individually equipped with an electric motor connected to an open differential. This could offer a potential configuration for the mass production of electric all-wheel-drive vehicles because of reduced cost and complexity. Although there is no torque vectoring or direct yaw moment control in this configuration, considerable handling improvement can be achieved by optimised front/rear torque distribution due to the longitudinal and lateral tire force coupling. In this study, a model predictive control design is presented with a coupled force prediction model for vehicle handling dynamics. The controller optimises the front/rear torque allocation to track the desired handling response and ensure vehicle stability. This study also compensates for actuator delay by incorporating the actuator dynamics into the control design. The performance of the proposed controller is evaluated through software simulations and experimental tests conducted on an electric all-wheel-drive Chevrolet Equinox.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".