Magneto-Rheological actuators for haptic devices: Design, modeling, control, and validation of a prototype clutch
Bibliographic record
Abstract
In our previous work [1], the potential benefits of Magneto-Rheological Fluid based actuators to the field of haptics were studied. Our results showed that the superior mechanical attributes of such actuators contribute to improvement of stability and transparency in haptic devices. To this end, a novel design of a small-scale MRF-based clutch, was proposed in [1]. This paper reports on the development and validation of the proposed MRF-based clutch. In addition, a closed-loop torque control strategy is presented. The feedback signal used in this control scheme comes from the magnetic field measurement and is used to compensate for the nonlinear behavior using an estimated model, based on Artificial Neural Networks (ANNs). Such a control strategy eliminates the need for torque sensors for providing feedback signals. The performance of the developed design and the effectiveness of the proposed modeling and control techniques are experimentally validated. The results clearly demonstrate that the clutch shows great potential for use in a multiple degrees-of-freedom (DOF) haptic interface for a class of medical applications.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".