Adaptive modeling of a fully hysteretic Magneto-Rheological clutch
Bibliographic record
Abstract
In this paper, a new open-loop model for a Magneto-Rheological (MR) based actuator is presented. The model consists of two parts relating the output torque of the actuator to its internal magnetic field, and the internal magnetic field to the applied current. Each part possesses its own hysteretic behavior. The first part uses an open-loop Bouc-Wen model to relate the output torque to internal magnetic field. The second part uses a novel nonlinear adaptive observer that relates the internal magnetic field to the applied current. The model facilitates accurate control of the actuator using its input current. It also eliminates the need for force/torque sensors for providing feedback signals. The accuracy of the constructed model is validated through simulations. The overall model as well as each part of it is assessed against a widely accepted hysteresis modeling approach, known as the Preisach model and its advantages are highlighted. The second part of model is also compared to Bingham model which has been broadly employed in modeling of MR fluid dynamic. Bouc-Wen model shows higher accuracy in capturing hysteretic behavior of MR fluid in comparison to non-hysteretic Bingham model. Experimental results using the prototyped actuation mechanism further verify the accuracy of the model and demonstrate its effectiveness.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".