A hybrid fuzzy logic proportional-integral-derivative and conventional on-off controller for morphing wing actuation using shape memory alloy Part 2: Controller implementation and validation
Bibliographic record
Abstract
Abstract The paper presents the numerical and experimental validation of a hybrid actuation control concept – fuzzy logic proportional-integral-derivative (PID) plus conventional on-off – for a new morphing wing mechanism, using smart materials made of shape memory alloy (SMA) as actuators. After a presentation of the hybrid controller architecture that was adopted in the Part 1, this paper focuses on its implementation, simulation and validation. The PID on-off 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. To implement the controller on the physical model, two programmable switching power supplies (AMREL SPS100-33) and a Quanser Q8 data acquisition card were used. The data acquisition inputs were two signals from linear variable differential transformer potentiometers, indicating the positions of the actuators, and six signals from thermocouples installed on the SMA wires. The acquisition board’s output channels were used to control power supplies in order to obtain the desired skin deflections. The experimental validation utilised an experimental bench test in laboratory conditions in the absence of aerodynamic forces, and a wind-tunnel test for different actuation commands. Simultaneously, the optimised aerofoils were experimentally validated with the theoretically-determined aerofoils obtained earlier. Both the transition point real time position detection and visualisation were realised in wind tunnel tests.
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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 | 0.000 |
| 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".