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Record W2802129616 · doi:10.1049/iet-pel.2017.0622

Control of a high‐voltage bidirectional dc–dc flyback converter for driving DEAs

2018· article· en· W2802129616 on OpenAlex
Ali Shagerdmootaab, Shahram Pourazadi, Mehrdad Moallem, Carlo Menon

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

VenueIET Power Electronics · 2018
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFlyback converterForward converterFlyback diodeControl theory (sociology)VoltageFlyback transformerElectrical engineeringControl (management)Computer scienceBoost converterEngineeringTransformer

Abstract

fetched live from OpenAlex

This study presents modelling and control of a high‐voltage ratio flyback converter for driving capacitive loads including smart material dielectric elastomer actuators (DEAs). These actuators find various applications including artificial muscles, optical devices, smart skin, and acoustics but require high actuation voltages. To this end, a high‐voltage bidirectional flyback converter for driving a capacitive load is studied in terms of modelling and control and applied to a DEA. The state‐space model of the converter is obtained for the capacitor charge and discharge modes when the converter operates in the continuous conduction mode and used to obtain a load voltage controller using the feedback linearisation method. The converter can be used to regenerate the capacitor charge into the dc source. The proposed hardware and control strategy was built and validated by driving DEA capacitive loads operating at high voltages around 4 kV. The experimental results are presented which validate the performance of the proposed converter and its control strategy.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score0.832

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 head0.203
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