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Record W2077265302 · doi:10.1115/imece2011-64160

Position Control of a Magnetically Levitated Robot Based on Magnetic Flux Measurement

2011· article· en· W2077265302 on OpenAlexaff
Moein Mehrtash, Naoaki Tsuda, Tatsuya Nobori, Mir Behrad Khamesee

Bibliographic record

VenueVolume 7: Dynamic Systems and Control; Mechatronics and Intelligent Machines, Parts A and B · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHall effect sensorPosition sensorMagnetic levitationPosition (finance)LaserRobotMagnetic fluxComputer scienceEngineeringMagnetic fieldMagnetAcousticsElectrical engineeringPhysicsOpticsArtificial intelligenceRotor (electric)

Abstract

fetched live from OpenAlex

Magnetic actuation has opened a new horizon in biological/biomedical applications. A novel magnetic actuation platform has been developed at Maglev Microrobotics Laboratory, University of Waterloo. In the previous work, laser sensors were used for positioning the levitated microrobot. This technique can be used only in transparent environment. In this paper, for applications in an enclosed environment, which may not be transparent, a novel position estimation method was proposed. The proposed method uses hall sensors, mounted on the disk pole-piece. The hall sensors’ optimal installation position has been investigated, and a function which relates hall sensors’ output and the position of robot was derived. Based on this function, position control of horizontal axis using hall sensors in place of laser sensor can be achieved. Usability of two dimensional controls in horizontal axis without laser sensors will be experimentally validated as future work of this research.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.197
Teacher spread0.187 · 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.

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
Published2011
Admission routes1
Has abstractyes

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