Robust control of synchromesh friction in an electric vehicle's clutchless automated manual transmission
Bibliographic record
Abstract
The aim of this study is to control the synchronizer operation in an automated manual transmission (AMT) in which the clutch is eliminated from the driveline to reduce the power losses. The goal of controlling the synchronization phase is to increase the lifetime of the synchronizer by establishing control over the frictional behaviour of such tribological system. The robust control approach starts by introducing the lubricated friction operating states and follows by modelling the dynamic system as well as the primary uncertainties affecting the synchronization phase. Considering the system uncertainties, a robust H∞multivariable controller is designed and the closed-loop performance is assessed by considering the noise and disturbance effects. The advantages of the proposed robust controller is discussed and compared with the performance of a PID controller which operates in the same closed-loop control configuration. The case study here is a synchronizer which is part of a 2-speed AMT designed for efficient gear shifting in an electric vehicle, with the purpose of improving the energy efficiency and enhancing the drive motor performance.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| 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".