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Record W4282835648 · doi:10.1115/1.4054779

On Prandtl–Ishlinskii Hysteresis Modeling of a Loaded Pneumatic Artificial Muscle

2022· article· en· W4282835648 on OpenAlexaff
Mohammad Al Saaideh, Mohammad Al Janaideh

Bibliographic record

VenueASME Letters in Dynamic Systems and Control · 2022
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsHysteresisControl theory (sociology)Prandtl numberArtificial musclePneumatic artificial musclesMaterials scienceMechanicsPhysicsComputer scienceActuatorControl (management)Condensed matter physicsHeat transferArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Modeling of the hysteresis nonlinearities of the pneumatic artificial muscle (PAM) is critical when it is proposed to drive various high-precision mechatronics applications. This letter aims to characterize the hysteresis nonlinearities obtained experimentally of the PAM system under the load force effect. The load-dependent Prandtl–Ishlinskii (LDPI) model with a load-dependent weight function is proposed to model the measured hysteresis loop under different load forces. Comparing the measured hysteresis loops to the estimated loops of the LDPI model demonstrates the proposed model’s ability to characterize the asymmetric and load force effects in the hysteresis nonlinearities of the PAMs.

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: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.457

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 head0.006
GPT teacher head0.186
Teacher spread0.181 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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