Stiffness control of a nylon twisted coiled actuator for use in mechatronic rehabilitation devices
Bibliographic record
Abstract
Mechatronic rehabilitation devices, especially wearables, have been researched extensively and proven to be promising additions to physical therapy, but most designs utilize traditional actuators providing unnatural, robot-like movements. Therefore, many researchers have focused on the development of actuators that mimic biological properties to provide patients with improved results, safety, and comfort. Recently, a twisted-coiled actuator (TCA) made from nylon thread has been found to possess many of these important properties when heated, such as variable stiffness, flexibility, and high power density. So far, TCAs have been characterized in controlled environments to define their fundamental properties under simple loading configurations. However, for an actuator like this to be implemented in a biomimetic design such as an exoskeleton, it needs to be characterized and controlled as a biological muscle. One major control law that natural muscles exhibit is stiffness control, allowing humans to passively avoid injury from external forces, or move the limbs in a controlled or high impact motion. This type of control is created by the antagonistic muscle arrangement. In this paper, an antagonistic apparatus was developed to model the TCAs from a biological standpoint, the stiffness was characterized with respect to the TCA temperature, and a fully functional stiffness and position controller was implemented with an incorporated TCA thermal model. The stiffness was found to have a linear relationship to the TCA temperatures (R2=0.95). The controller performed with a stiffness accuracy of 98.95% and a position accuracy of 92.7%. A final trial with varying continuous position input and varying stepped stiffness input exhibited position control with R2=0.9638.
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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 | 0.000 | 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".