On–off and proportional–integral controller for a morphing wing. Part 2: Control validation – numerical simulations and experimental tests
Bibliographic record
Abstract
The second part of this article describes the numerical simulation and experimental validations of actuators control system for a morphing wing application, which was developed and designed in the first part of this article. After the description of the finally adopted control architecture, the validation for the non-linear system model is presented. First, the integrated controller is validated numerically with MATLAB/Simulink software, followed by a physical implementation of the control and experimental validation in the wind tunnel. To implement the controller on the physical model, two programmable switching power supplies, AMREL SPS100-33, and Quanser Q8 data acquisition card were used. The inputs of the data acquisition card were the two signals issued by the linear variable differential transformer potentiometers, indicating the positions of the actuators, and the six signals recorded by thermocouples installed on the SMA wires. The acquisition board output channels were used to control the required power supply to obtain the desired skin deflections. The control experimental validation was performed first on a bench test and then in the wind tunnel test. A number of optimized airfoil shapes, used in the design phase, were translated into actuators vertical displacements which were used as input signals for the controller. In the wind tunnel tests, a comparative study was realized around the transition point position for the reference airfoil and for each optimized airfoil.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".