Adaptive nth Order Lookup Table used in Transmission Double Swap Shift Control
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Bibliographic record
Abstract
<div class="htmlview paragraph">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 <i>n</i> rows and <i>m</i> 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 <i>n</i> 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.</div>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it