Accurate Clutch Slip Controllers During Vehicle Steady and Acceleration States
Bibliographic record
Abstract
Over the past years, the control of the clutch clamping force has been studied to guarantee a smooth/fast and low-wear engagement. Recent studies have highlighted the interest in controlling the clutch clamping force in order to limit vibrations in vehicle drivelines. However, the major risk with any clutch clamping control strategy is an unexpected clutch opening due to the ignorance of the nonlinear and time-varying relationship between clutch clamping and clutch slip. Inspite of improvements, an accurate clutch slip control currently remains a challenge due to high nonlinear dynamics, uncertain parameters, and noisy environments, which render the clutch slip control more complex. In line with this challenging premise, this study presents two accurate clutch slip controllers used during vehicle steady states (constant engine speed) and vehicle acceleration states (increasing engine speed). The first controller, based on punctual least square adaptations of a clutch slip relation, yielded accurate clutch slip tracking results only in the vehicle steady state. In contrast, the second controller, based on a nearly continuous least mean square adaptation of the clutch slip relationship in parallel with a proportional-integral compensator, yielded accurate clutch slip tracking results both in vehicle steady and acceleration states.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| 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".