Soft‐computing techniques for cruise controller tuning for an off‐road electric vehicle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Speed controllers may be employed to provide safer along with more secure vehicles. They may also be used to minimise environmental pollution, e.g. speed controllers can be employed to track speed optimal velocity profile based on energy consumption minimisation. Consequently, accurate speed tracking is important. However, despite soft‐computing techniques have been proved successful in controller tuning, there is a limited amount of research on these techniques applied to speed controller optimisation. Therefore, this study performs a comparison study on PI cruise controller tuning for an off‐road electric vehicle. A cost function is designed to reach an accurate EV speed tracking while considering safety aspects, such as no reverse speed. The ACO‐NM algorithm has been demonstrated to be the most efficient compared to GA, ALO, DE, and PSO. Indeed, ACO‐NM reached high‐quality solutions for lower computational cost for three driving cycles. Moreover, contrary to the majority of published work on the subject, experimental validations have been carried out with the optimised PI cruise controllers. The experimental results have validated the ACO‐NM efficiency with a maximum overshoot average <10% for the hardest acceleration of the real driving cycle.
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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) | 0.000 | 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 | 0.000 |
| 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