Adaptive neuro-fuzzy controllers for an open-loop morphing wing system
Bibliographic record
Abstract
Abstract A new method for the realization of two neuro-fuzzy controllers for a morphing wing design application is presented here. The controllers' main function is to correlate each set of pressure differences, calculated between the optimized and the reference airfoil, with each of the airfoil deformations produced by the actuators' system. The pressures are calculated at different chord positions and will also be measured during wind tunnel tests. During a first identification phase, the two fuzzy inference systems (FISs) from the controllers' structure are generated for 16 flight conditions characterized by Mach numbers and angles of attack. Next, the FIS are optimized with the Matlab function adaptive neuro-fuzzy inference system (ANFIS) by training over different epochs. Finally, the controllers are validated for the other 33 flight conditions of the open-loop morphing wing system. This is the first time that such a method of relating the pressure differences to airfoil displacements has been conceived and used in an open-loop morphing wing controller system.
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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 | 0.001 | 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".