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Feedback Control of a Two-Degree-of-Freedom Electromagnetic Reluctance Precision Motion System

2025· article· en· W4416748562 on OpenAlexaff
Michael Pumphrey, Mohammad Al Saaideh, Natheer Alatawneh, Mohammad Al Janaideh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsMemorial University of NewfoundlandUniversity of Guelph
Fundersnot available
KeywordsControl theory (sociology)ActuatorTorqueSolenoidMagnetic reluctanceMagnetTranslation (biology)Rotation (mathematics)Rotation around a fixed axisHinge

Abstract

fetched live from OpenAlex

This study investigates a novel Xθ actuation system driven by a reluctance actuator (RA) and two accompanying moving magnet actuators (MMAs). The system enables precise control of both translational (x) and rotational (θ) motion, offering a two-degree-of-freedom (2DOF) solution for high-precision applications. The two MMAs introduce additional force and torque dynamics through the solenoid and permanent magnet (PM) pairs. Flexure hinges assist with the retraction force of the mover element, providing the necessary stiffness without introducing frictional effects. The system was modeled analytically, optimized, and validated experimentally with a developed feedback and feedback control, achieving steady-state errors of approximately ±7 µm in x translation and ±0.3 mrad in θ rotation which can be attributed to systematic errors in the sensor itself. The most relevant application is the fastscan mirror in extreme ultraviolet (EUV) lithography where specific targeted rotational and translational trajectories can benefit light beam positioning, such as wavefront corrections. This system allows translation and rotation specifications to be realized in one actuation unit, opening up more design possibilities for controlling precision motion systems.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.194
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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