Soft Actor-Critic Based Controller for Clutch Fill and Engagement
Bibliographic record
Abstract
Clutch fill and engagement control has posed a long-standing challenge in transmission research due to the inherent trade-off between fast response and low torque disturbance. Calibration can improve shift quality and efficiency, but they are labor-intensive, system-specific, and often lack robustness to varying operating conditions. This paper presents a closedloop reinforcement learning controller for wet clutch fill and engagement. The proposed method employs a soft actor-critic (SAC) algorithm trained on a physics-based clutch simulator, enabling the controller to balance rapid filling with smooth torque transfer. Unlike conventional calibration strategies, the reinforcement learning framework adapts to system dynamics and reduces dependence on manual tuning. Simulation results demonstrate that the controller achieves both short fill times and low engagement jerk, meeting the primary criteria for optimal clutch performance. The findings suggest that reinforcement learning offers a promising pathway toward automated, adaptable, and cost-effective control strategies for modern automatic and hybrid transmission systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".