Advanced $X \theta$ Reluctance Electromagnetic Micropositioning System for Precision Motion Control
Bibliographic record
Abstract
This study examines a novel setup of a micropositioning trajectory manipulator in$X \theta$, energized by a reluctance actuator (RA) and two accompanying moving magnet actuators (MMA). The design is characterized by a C -core RA, which features asymmetrical air gaps between the mover and the stator elements when under angular$\theta$rotation. When the stator coil is energized, a magnetic flux induces a force in the mover. Two MMAs can add force and torque dynamics to the system via solenoid and permanent magnet (PM) pairs to offer additional corrective actions. Facilitating control of a translational ($x$) and rotational ($\theta$) two-degree-of-freedom (2DOF) actuation system. Flexure hinges aid in the retraction force of the mover element and provide needed stiffness to the system without frictional effects. This was modeled analytically and optimized to achieve outlined performance objectives. The system was validated experimentally through triangle, and sinusoidal trajectories in open loop control. The most relevant application is scanning mirror systems where specific targeted rotational and translational trajectories can benefit light beam positioning. This system allows both translation and rotation specifications of a selected trajectory to be realized in one actuation unit, opening up more design possibilities for controlling precision positioning systems.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".