Adaptive nth Order Lookup Table used in Transmission Double Swap Shift Control
Bibliographic record
Abstract
The new Chrysler six-speed transaxle makes use of an underdrive assembly to extend a four-speed automatic transmission to six-speed. It is achieved by introducing double-swap shifts. During double-swap shift, learning the initial clutch torque capacity of the underdrive assembly's subsystem has a direct impact on the shift quality. A new method is proposed to compute and learn the initial clutch torque capacity of the releasing element. In this paper, we will outline a new mathematical method to compute and learn the accurate starting point of the clutch torque capacity for double swap shift control. The performance of the shift is demonstrated and the importance of the adaptation to shift quality is highlighted. An nth order lookup table is presented; this table contains n rows and m columns. Every row defines a relationship between the dependent variable such as actuator duty cycle and one independent variable such as transmission oil temperature, input torque or battery voltage. For given values of the independent variables, one dependent variable is computed as a function of weighted linear combination of n different interpolations. An example is given to calculate the initial duty cycle based on two independent variables (transmission oil temperature and the input torque). Based on shift results, this method is demonstrated to be effective, and accurate.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".