Characterization, modeling, and control of Ni-Ti shape memory alloy based on electrical resistance feedback
Bibliographic record
Abstract
The use of shape memory alloy actuators has steadily increased within the fields of aerospace, robotics, and biomedical engineering due to their superior properties compared to other actuation systems. Position control of shape memory alloy actuators is difficult due to the highly non-linear behavior but has been well studied using numerous approaches. Electrical resistance can be used to estimate strain in shape memory alloy actuator wire due to a correlation between the two parameters. Previous models of this correlation are subject to one or more drawbacks such as being limited to a single applied load, not accounting for hysteresis effects, or applying only to a specific actuator size. This article presents a stress–strain–resistance model that accounts for varying applied load, major and minor hysteresis effects and is normalized in terms of actuator geometry. Results of simulation and a simple position control experiment are demonstrated, validating the performance of the model. Furthermore, a correlation between the model and an augmented version of the Liang and Rogers model is also presented.
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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) | 0.000 | 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) | 0.001 | 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".