New methodology for the controller of an electrical actuator for morphing a wing
Bibliographic record
Abstract
The modeling, simulation and control of an electrical actuator used for morphing wing are presented. Because of its small size, this actuator belongs to the category of miniature actuators. Before proceeding with the modeling, the actuator was tested experimentally to ensure that the entire range of the requirements (rated or nominal torque, nominal current, nominal speed, static force, size) would be fulfilled. The complete electromechanical energy miniature actuator was designed in-house, as there is no actuator on the market that could fit directly inside the wing model. The wing model is a portion of an existing regional aircraft. The miniature electrical actuator consists of a brushless direct current (BLDC) motor with a gearbox and a screw for pushing and pulling the flexible upper surface of the wing. The electrical motor and the screw are coupled through a gearing system. The first part of the paper gives a literature review of the actuators used for morphing wing. The theoretical analysis of the BLDC motor and its gearbox are presented in the second part. The design of both controllers (a current and a positioning controller) used to improve the dynamic behavior of the actuator are presented in the third part. The experimental results of the numerical analysis will be presented in a subsequent paper. The numerical analysis is done with MATLAB/SIMULINK, and a Digital Signal Processor (DSP) will be used for experimental evaluation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".