Adaptive Temperature Control of NiTi Shape Memory Alloy (SMA) for Dynamic Environmental Thermal Convection
Bibliographic record
Abstract
In recent years, there have been an increased interest in manufacturing and controlling of smart materials such as NiTi Shape Memory for variety of applications in the fields of robotics, automotive, aerospace, and biomedical. In the past, there have been several studies done to control the position of SMA actuators, they mostly use a position sensor which directly measures the position [1][2]. The use of a position sensor adds substantially to the cost of an actuator unit. Moreover, there have been a few studies done in the field of self-sensing control of SMA actuators, however, they either assume a constant environmental conditions[3] or don’t consider the full complexity of the hysteresis behavior of SMAs [4]. Shape memory effect of SMAs, such as NiTi, are inherently due to a thermal process which leads to a phase transformation between the two solid phases of austenite and martensite [5]. The objective of this paper is to develop an adaptive electrical resistance feedback controller which controls the temperature of the NiTi SMA actuator based on the identified unknown heat convection coefficient.
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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".