On–off and proportional–integral controller for a morphing wing. Part 1: Actuation mechanism and control design
Bibliographic record
Abstract
The main objective of this research work is the development of an actuation control concept for a new morphing actuation mechanism made of smart materials, which is built from a shape memory alloy (SMA). Two lines of smart actuators were incorporated to a rectangular wing to modify the upper wing surface, made of a flexible skin, with the intention to move the laminar-to-turbulent transition point closer to the wing trailing edge. After a brief introduction of the morphing wing system architecture and requirements, the actuation lines' design and instrumentation are presented. The integrated controller controls the SMA actuators via an electrical current supply, so that the transducer position can be used to eliminate the deviation between the required values for vertical displacements (corresponding to the optimized airfoils) and their physical values. The final configuration of the integrated controller is a combination of a bi-positional (on–off) controller and a PI (proportional–integral) controller, due to the two heating and cooling phases of the SMA wires' interconnection. This controller must behave like a switch between the cooling and the heating phases, situations where the output current is 0 A, or is controlled by a PI type law. The PI controller for the heating phase is optimally tuned using integral and surface minimum error criteria (Ziegler–Nichols). The controller is numerically tested on the linear identified system in terms of time response, Bode diagram, amplitude and phase stability margins, and root-locus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".