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Record W4226342812 · doi:10.1109/tie.2022.3163553

Development of a Butterfly Fractional-Order Backlash-Like Hysteresis Model for Dielectric Elastomer Actuators

2022· article· en· W4226342812 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2022
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsConcordia University
FundersNational Natural Science Foundation of China
KeywordsBacklashHysteresisControl theory (sociology)ActuatorDifferential equationMaterials scienceComputer sciencePhysicsMathematicsMathematical analysisArtificial intelligenceCondensed matter physics

Abstract

fetched live from OpenAlex

Differential equation-based hysteresis model is an efficient approach to predict the hysteresis effect with less modeling parameters. Available differential equation-based hysteresis models mainly focus on describing the single-loop hysteresis effect and fail to describe the butterfly hysteresis effect. In this article, a butterfly fractional-order backlash-like hysteresis model is proposed to describe the butterfly hysteretic behavior. To this end, a twist mechanism is developed to transform the single-loop backlash-like hysteresis model into the one that is capable of representing the butterfly hysteresis. To further improve the modeling accuracy, a fractional-order expression is integrated into the butterfly backlash-like hysteresis model. In addition, considering the buckling threshold characteristic of the elastomer membrane in dielectric elastomer actuators, a buckling threshold selector is designed and incorporated into the model as well. Experiments are conducted to validate the effectiveness of the proposed model.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.219
Teacher spread0.197 · 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