A new morphing wing mechanism using smart actuators controlled by a self-tuning fuzzy logic controller
Bibliographic record
Abstract
The paper presents the design of an intelligent actuation control concept, self-tuning fuzzy logic Proportional-Integral-Derivative plus conventional On-Off, for a new morphing mechanism using smart materials made of Shape Memory Alloy (SMA) for the actuators. In this way, two fuzzy inference systems were developed and implemented in the self-tuning fuzzy logic controller. In the design phase, the input-output mappings of the fuzzy models were designed and a final architecture for the hybrid controller was obtained. The shapes chosen for inputs membership functions of both fuzzy inference systems were s-functions, π-functions, respectively z-functions, while the product fuzzy inference and the center average defuzzifier were applied (Sugeno). The obtained controller was numerically and experimentally implemented using the Matlab/Simulink software. Following preliminary numerical simulations which were conducted to tune the controller, an experimental validation was performed: bench tests and wind tunnel tests. Simultaneously, the aerodynamic part of the morphing wing model was validated: optimized airfoils were experimentally validated with the theoretically-determined airfoils obtained earlier. Also, the transition point real time position detection and visualization were realized in wind tunnel tests. © 2011 by Ruxandra Mihaela Botez. Published by the American Institute of Aeronautics and Astronautics, Inc.
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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 | 0.000 |
| 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".