A Computationally Efficient Hysteresis Model for Magneto-Rheological Clutches and Its Comparison with Other Models
Bibliographic record
Abstract
The collaborative robot market has experienced rapid growth, leading to advancements in compliant actuation and torque control. Magneto-rheological (MR) clutches offer a hardware-level solution for achieving both compliance and torque control through adjustable coupling between the input and output of the MR clutch. However, the presence of frequency-dependent magnetic hysteresis makes controlling the output torque challenging. In this paper, we present a comparative study of six widely used hysteresis models and propose a computationally efficient algebraic model to address the issue of hysteresis modeling and control of the output torque of rotary MR clutches. We compare the estimated torques with experimental measurements from a prototype MR clutch, to evaluate the computational complexity and accuracy of the model. Our proposed algebraic hysteresis model demonstrates superior accuracy and approximately two times less computational complexity than the Bouc–Wen model, and approximately twenty times less memory requirement than neural network-based models. We show that our proposed model has excellent potential for embedded indirect torque control schemes in systems with hysteresis, such as MR clutches and isolators.
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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 | 0.001 |
| 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".