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Record W2152661282 · doi:10.1109/coase.2010.5584003

Optimal motion control of magnetically levitated microrobot

2010· article· en· W2152661282 on OpenAlexaff
Moein Mehrtash, Mir Behrad Khamesee

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsLevitationElectromagnetMagnetic levitationMotion controlWorkspaceControl theory (sociology)Magnetic fieldYoke (aeronautics)FlywheelRange (aeronautics)Control systemMagnetPhysicsComputer scienceMechanical engineeringEngineeringRobotElectrical engineeringControl (management)Artificial intelligenceAerospace engineering

Abstract

fetched live from OpenAlex

This paper deals with the three-dimensional (3-D) manipulation of a microrobot with levitation technology. A magnetic drive unit is developed to generate the magnetic field for propelling the microrobot in an enclosed environment. The drive unit consists of electromagnets, a disc pole-piece for connecting the magnetic poles, and a soft iron yoke. To handle 3-D high precision motion control of the microrobot, experimental magnetic field measurements coupled with numerical analysis were done to identify the dynamic model of levitation. This approach leads the design of linear quadratic integral (LQI) control system, based on the derived state-space model. The 3-D motion control capability of the LQI control method is verified experimentally, and it was demonstrated that the microrobot can be operated in the levitation system workspace, vertical range of 30 mm and the horizontal range of 20 × 20 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , with 10 μm resolution.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.997

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)0.0040.000

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 head0.211
Teacher spread0.207 · 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 designBench or experimental
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

Citations4
Published2010
Admission routes1
Has abstractyes

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