Magnet-Coil Role-Switching Real-Time Wrench Model for Magnetic Levitated Motors With Extendable Motion Range
Bibliographic record
Abstract
Magnetic levitation motors are broadly employed for haptic devices and precision manufacturing. The motor developed in this study comprises a disc-magnet mover and stating square coils. Such actuators are commonly controlled using lookup tables, which require large amounts of memory and time-consuming offline preparation. To accurately calculate the system wrench in real time, a novel wrench model is proposed by considering the disc magnet and the square coils as thin-walled solenoid and multiple stacked magnets, respectively. The Gauss–Legendre quadrature and magnetic surface charge approach are utilized to numerically estimate the Lorentz force and torque. The role-switching model computes the wrench matrix between a single disc magnet and nine coils in 96.5$\boldsymbol{\mu}$s via parallel computing. Considering the system current limits, the weighted pseudoinverse is incorporated for commutation to further extend the motion range. The model accuracy is evaluated and verified through the Frobenius norm and load cell measurements. The experimental results show that the motion resolutions are$\boldsymbol{\pm}$10$\boldsymbol{\mu}$m and$\boldsymbol{\pm}$0.02${}^{\circ}$in the translational and rotational axes, respectively. The trajectory tracking results show that the magnetic levitation motor can translate the mover across the platform with a 380 mm side length limited only by the platform geometry, and the mover can be levitated up to a noticeably large air gap of 65 mm and rotated up to an extensive range of$\boldsymbol{\pm}$45${}^{\circ}$.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".