Analytical Stiffness Modeling and Experimental Validation for a Pneumatic Artificial Muscle
Bibliographic record
Abstract
Since the introduction of assistive technologies for enhancing human mobility, there has been a high demand for compact, lightweight, powerful and energy efficient actuator. The Pneumatic Artificial Muscle (PAM) is a distinctive compliant pneumatic actuator, which has properties similar to the biological skeletal muscle, making it a great candidate for applications in human mobility assistive devices. Whereas the PAMs can be used actively or passively, until now, it has been mostly used in active applications to power various mechanisms, such as robotic arms, most notably the Shadow Robot Company Dextrous hand. For those applications, static and dynamic models of PAM have been developed by researchers to fairly accurately predict the muscle-force carrying capabilities and muscle contraction distance behavior, respectively. However, limited passive models have been developed, with results varying from acceptable to poor in terms of accuracy. Recognizing the significance of characterizing the PAM passive behavior, especially for legged locomotion application, this paper proposes a PAM stiffness model that is based on Newtonian mechanics and considers geometric, mechanical and material properties of the muscle. The proposed stiffness model is experimentally validated for a wide range of operating conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".