Constrained control of the synchromesh operating state in an electric vehicle's clutchless automated manual transmission
Bibliographic record
Abstract
This paper considers the constrained control problem of the friction regimes in sliding lubricated surfaces with the purpose of speed synchronization, wear reduction and increasing the lifetime of the friction lining material. The case study here is the engagement process of the synchronizer cone clutch system. Such synchronizer performs the clutchless gear shifting in a 2-speed automated manual transmission (AMT) of an electric vehicle. In the present study, the frictional behavior of the cone clutch system is investigated by considering the involved lubricated friction regimes. By knowing the lubricated sliding friction regimes, the dynamic model of the system is derived according to the variable coefficient of friction. Moreover, the primary sources of the uncertainty and disturbance are recognized and considered in the dynamic model of the system. For the purpose of controlling the system, the control objectives and the constraints are defined, and a controller design method is proposed. The controller design method is based on solving a set of linear programming (LP) problems in the offline phase, which results in a piecewise affine (PWA) feedback law that can be easily applied on the system in the real-time closed-loop configuration. Finally, the performance of the proposed control approach is assessed by presenting the closed-loop control results for the ideal situation as well as the perturbed systems in the presence of the disturbance.
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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 | 0.001 |
| 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".