Adaptive Control of a Hysteretic Magnetorheological Robot Actuator
Bibliographic record
Abstract
In this paper, a new adaptive control scheme is proposed to compensate for the magnetic hysteresis in magnetorheological (MR) fluid-based actuators. MR actuators offer high torque-to-mass and torque-to-inertia ratios. Input and output shafts are mechanically decoupled in MR actuators, providing lower inertia compared to the same power geared motors. Additionally, geared motors typically add significant noises on the torque/force measurements. Noises on the torques/forces can be attenuated in MR actuators due to fluidic connections between the input and output shafts, facilitating high fidelity torque/force control. Despite these unique characteristics, magnetic circuits within MR actuators create hysteresis between the input current and output torque that negatively affects the quality of torque/force control and the repeatability of MR actuators. A hysteresis compensation scheme is essential to gain repeatable and high-quality actuation. To this end, we propose an adaptive control method that estimates both hysteresis and uncertain parameters of the magnetic circuit and cancels nonlinearities based on feedback linearization technique. A set of experiments is performed to validate the effectiveness of the proposed method, and the results are compared to a proportional-integral-derivative controller.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".