A Self–Tuning Intelligent Controller for a Smart Actuation Mechanism of a Morphing Wing Based on Shape Memory Alloys
Bibliographic record
Abstract
The paper exposes some of the results obtained in a major research project related to the design, development, and experimental testing of a morphing wing demonstrator, with the main focus on the development of the automatic control of the actuation system, on its integration into the experimental developed morphing wing system, and on the gain related to the extension of the laminar flow over the wing upper surface when it was morphed based on this control system. The project was a multidisciplinary one, being realized in collaboration between several Canadian research teams coming from universities, research institutes, and industrial entities. The project’s general aim was to reduce the operating costs for the new generation of aircraft via fuel economy in flight and also to improve aircraft performance, expand its flight envelope, replace conventional control surfaces, reduce drag to improve range, and reduce vibrations and flutter. In this regard, the research team realized theoretical studies, accompanied by the development and wind tunnel experimental testing of a rectangular wing model equipped with a morphing skin, electrical smart actuators, and pressure sensors. The wing model was designed to be actively controlled so as to change its shape and produce the expansion of laminar flow on its upper surface. The actuation mechanism used to change the wing shape by morphing its flexible upper surface (manufactured from composite materials) is based on Shape Memory Alloys (SMA) actuators. Shown here are the smart mechanism used to actuate the wing’s upper surface, the design of the intelligent actuation control concept, which uses a self–tuning fuzzy logic Proportional–Integral–Derivative plus conventional On–Off controller, and some of the results provided by the wind tunnel experimental testing of the model equipped with the intelligent controlled actuation system. The control mechanism uses two fuzzy logic controllers, one used as the main controller and the other one as the tuning controller, having the role of adjusting (to tune) the coefficients involved in the operation of the main controller. The control system also took into account the physical limitations of the SMA actuators, including a software protection section for the SMA wires, implemented by using a temperature limiter and by saturating the electrical current powering the actuators. The On–Off component of the integrated controller deactivates or activates the heating phase of the SMA wires, a situation when the actuator passes into the cooling phase or is controlled by the Self–Tuning Fuzzy Logic Controller.
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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".