Mechanism of bandwidth improvement in passively cooled SMA position actuators
Bibliographic record
Abstract
The heating of shape memory alloy (SMA) materials leads to a thermally driven phase change which can be used to do work. An SMA wire can be thermally cycled by controlling electric current through the wire, creating an electro-mechanical actuator. Such actuators are typically heated electrically and cooled through convection. The thermal time constants and lack of active cooling limit the operating frequencies. In this work, the bandwidth of a still-air-cooled SMA wire controlled with a PID controller is improved through optimization of the controller gains. Results confirm that optimization can improve the ability of the actuator to operate at a given frequency. Overshoot is observed in the optimal controllers at low frequencies. This is a result of hysteresis in the wire's contraction–temperature characteristic, since different input temperatures can achieve the same output value. The optimal controllers generate overshoot during heating, in order to cause the system to operate at a point on the hysteresis curve where faster cooling can be achieved. The optimization results in a controller which effectively takes advantage of the multi-valued nature of the hysteresis to improve performance.
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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 | 0.001 | 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".