A Neural Network Controller New Methodology for the ATR-42 Morphing Wing Actuation
Bibliographic record
Abstract
A morphing wing model is used to improve aircraft performance.To obtain the desired airfoils, electrical actuators are used, which are installed inside of the wing to morph its upper surface in order to obtain its desired shape.In order to achieve this objective, a robust position controller is needed.In this research, a design and test validation of a controller based on neural networks is presented.This controller was composed by a position controller and a current controller to manage the current consumed by the electrical actuators to obtain its desired displacement.The model was tested and validated using simulation and experimental tests.The results obtained with the proposed controller were compared to the results given by the PID controller.Wind tunnel tests were conducted in the Price-Païdoussis Wind Tunnel at the LARCASE laboratory in order to calculate the pressure coefficient distribution on an ATR-42 morphing wing model for different flow conditions.The pressure coefficients obtained experimentally were compared with their numerical values given by XFoil software.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".