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Record W2789888630 · doi:10.1109/lra.2018.2803815

The Programmable Permanent Magnet Actuator: A Paradigm Shift in Efficiency for Low-Speed Torque-Holding Robotic Applications

2018· article· en· W2789888630 on OpenAlexafffund
Jean-Baptiste Chossat, Alexis Maslyczyk, Jean-Simon Lavertu, Vincent Duchaine

Bibliographic record

VenueIEEE Robotics and Automation Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsActuatorTorqueMagnetStatorRotary actuatorPower (physics)Electrical engineeringMechanical engineeringComputer scienceControl theory (sociology)Automotive engineeringEngineeringPhysicsControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

In this letter, we propose a new type of electromagnetic actuator called the “programmable permanent magnet” (PPM) actuator. The PPM actuator is designed to tackle low-speed high-torque applications where classic electromagnetic actuators are highly inefficient. The PPM actuator is based on the application of pulsed current to magnetize a hard ferromagnetic stator that is made of individual SmCo (custom grade) magnets placed in a Halbach array configuration. The motor creates a springlike torque function that generates up to 0.2 N·m. Unlike electromagnetic actuators, by design, the PPM actuator does not draw current while exerting torque. Instead, the motor's power consumption depends on its rotational speed or torque variations. The PPM actuator is, by consequence, most efficient, whereas classical electromagnetic motors are most inefficient, and is a new alternative to actuators for robotic applications, which tend to operate in low-speed, high-torque conditions. The prototyped PPM actuator measures 47 mm in diameter, with a height of 35 mm and weight of 200 g, and has been successfully integrated in a commercially available 2-finger gripper from Robotiq, Inc.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.878
Threshold uncertainty score0.584

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 head0.010
GPT teacher head0.235
Teacher spread0.225 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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