Linear torque actuation using FPGA-controlled Magneto-Rheological actuators
Bibliographic record
Abstract
In recent years, Magneto-Rheological (MR) clutches have been increasingly used for realizing compliant actuation. One difficulty in using MR clutches is the existence of nonlinear hysteretic behaviors between the input current and output torque of an MR clutch. In this paper, a new closed-loop, Field-Programable-Gate-Array (FPGA) based control scheme to linearize an MR clutch's input-output relationship is presented. The feedback signal used in this control scheme is the magnetic field acquired from hall sensors within the MR clutch. The FPGA board uses this feedback signal to compensate for the nonlinear behavior of the MR clutch using an estimated model of the clutch magnetic field. The local use of an FPGA board will dramatically simplify the use of MR clutches for torque actuation. The effectiveness of the proposed technique is validated using an experimental platform that includes an MR clutch as part of a compliant actuation mechanism. The results clearly demonstrate that the use of the FPGA based closed-loop control scheme can effectively eliminate hysteretic behaviors of the MR clutch, allowing to have linear actuators with predictable behaviors.
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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) | 0.002 | 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".