Synthesis and Experimental Validation of Two-Step Variable-Structure Control of a Micro-Actuated Flow Effector
Bibliographic record
Abstract
To achieve fast and agile missile flight, carefully designed micro-actuated flow effectors relying on smart structures is a promising technology thanks to their compactness and lightweight properties. Indeed, micro-actuated flow effectors are ideal candidates to carry out active flow control. Such effectors can be mounted on a compliant mechanism and be actuated by means of antagonistic shape memory alloy (SMA) wires. This paper proposes a variable-structure control law for such micro-actuated flow effectors which is designed with the objective of controlling flow effector positions over a range of 1 mm and within a bandwidth of [0.1 Hz, 1 Hz]. A bang-bang controller is used to guarantee fast decaying transients and performance robustness with respect to modeling uncertainties. When the tracking error is less than a prescribed threshold, stabilization without chattering is achieved by switching from the bang-bang control law to a linear digital control law, whose design is based on the parametric identification of the entire mechanical device composed of the antagonistic actuation, of the compliant mechanism, and of the micro-actuated flow effector. The rate independent hysteresis, which is typical of SMA, is represented as an input-output phase lag and is thus taken into account in the control design. Experimental results show that the control law is capable of providing effective control up to 1.0 Hz.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".