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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".