Adaptive hysteresis compensation for a magneto-rheological robot actuator
Bibliographic record
Abstract
In this paper, adaptive compensation of the hysteresis in a Magneto-Rheological (MR) fluid based actuators and its application for sensor-less high fidelity force/torque control is investigated. The MR actuator considered in this paper was originally described in [1] and [2]. This actuator offers high torque-to-mass and torque-to-inertia ratios. Yet, as an essential component of MR actuators, the magnetic circuit of the actuator shows hysteresis between its input current/voltage and output magnetic field. The hysteresis in the magnetic circuit results in a similar relationship between the input current and the output torque of the MR actuator. The control scheme used with actuators possessing hysteresis often requires compensating for the hysteresis. To this end, we propose an adaptive control method based on feedback linearization that estimates both hysteresis and uncertain parameters of the magnetic circuit. A set of experiments is performed to validate the effectiveness of the proposed method.
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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".