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Record W1974011937 · doi:10.1017/s0263574709990324

A shape memory alloy based tendon-driven actuation system for biomimetic artificial fingers, part II: modelling and control

2009· article· en· W1974011937 on OpenAlexaff
Gabriele Gilardi, Edmund Haslam, Vishalini Bundhoo, Edward J. Park

Bibliographic record

VenueRobotica · 2009
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsSimon Fraser UniversityUniversity of Victoria
Fundersnot available
KeywordsShape-memory alloyArtificial muscleSMA*Control theory (sociology)ActuatorPID controllerComputer scienceJoint stiffnessController (irrigation)TendonMaterials scienceEngineeringStructural engineeringArtificial intelligenceMechanical engineeringStiffnessControl (management)AnatomyAlgorithm

Abstract

fetched live from OpenAlex

SUMMARY In this paper, the dynamics and biomimetic control of an artificial finger joint actuated by two opposing one-way shape memory alloy (SMA) muscle wires that are configured in a double spring-biased agonist–antagonist fashion is presented. This actuation system, which was described in Part I, forms the basis for biomimetic tendon-driven flexion/extension and abduction/adduction of the artificial finger. The work presented in this paper centres on thermomechanical modelling of the SMA wire, including both major and minor hysteresis loops in the phase transformation model, and co-operative control strategy of the agonist–antagonist muscle pair using a pulse-width-modulated proportional-integral-derivation (PWM–PID) controller. Parametric analysis and identification are carried out based on both simulation and experimental results. The performance advantage of the proposed co-operative control is shown using the metacarpophalangeal joint of the artificial finger.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.033
GPT teacher head0.244
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations54
Published2009
Admission routes1
Has abstractyes

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