Ride comfort control of in-wheel motor drive unmanned ground vehicles with energy regeneration
Bibliographic record
Abstract
Adopting in-wheel motor drive can improve vehicle dynamics control functions, which is the most ideal drive mode of unmanned ground vehicle. However, with the increase of the heavy unspring-mass vibration energy while the vehicle running on uneven road, the ride comfort will be seriously deteriorated. To solve the problem and save energy, the ride comfort control based on regenerative suspensions is adopted. By analyzing the vibration performance, the adverse effects of the vehicle equipped in-wheel motors with passive suspensions are revealed. Then, the dynamics model of the regenerative suspension is built. Based on the suspension power recovery, the multi-state optimal control strategy for improving the ride comfort is designed. Finally, comparing the simulation results of regenerative suspensions with the test results of passive suspensions, when the vehicle mass ratio decreases from 8:1 to 4:1, the body acceleration and the root mean square value of tire dynamic load increase by 28.1% and 31.6%, correspondingly. With the control method, the body acceleration is decreased by 23% and reaches the level of conventional vehicles. Furthermore, part of the vehicle vibration energy can be recovered and the vehicle driving range can be extended.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".