Hysteresis modeling of a hybrid magneto-rheological actuator
Bibliographic record
Abstract
A hybrid MR clutch utilizes a permanent magnet and an electromagnetic coil to generate the magnetic field within the clutch pack. The existence of nonlinear hysteretic behavior between the input and output of the MR clutches needs to be fully investigated in order to perform high fidelity force/torque control. In this paper, a closed-loop torque control strategy is presented. The feedback signal used in the closed-loop control is estimated using an Artificial Neural Network (ANN) that uses magnetic field measurements from an embedded Hall sensor inside the clutch. The developed neural network is capable of accurately predicting the transmitted torque as well as modeling the hysteretic relationship between the applied current, internal magnetic field within the clutch, and the output torque of the actuator. This technique introduces a cost effective force/torque control by eliminating the need for conventional torque sensors for providing feedback. The performance of the trained neural network and the said control strategy are experimentally validated. The results clearly show the ability of a hybrid MR clutch in delivering torque tracking control with high fidelity required in many human-safe actuation systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".