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Record W4200226252 · doi:10.1109/ee53374.2021.9628310

Minimum Deviation Controller for Indirect Energy Transfer Converters

2021· article· en· W4200226252 on OpenAlexaff
Ksenija Josipovic, Aleksandar Prodić, Liangji Lu, Gianluca Roberts, Giacomo Calabrese, Florian Neveu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsControl theory (sociology)ConvertersCapacitorPID controllerController (irrigation)VoltageTransfer functionBandwidth (computing)Duty cycleOpen-loop controllerTransient responseComputer scienceEngineeringElectrical engineeringControl engineeringTemperature controlClosed loopControl (management)

Abstract

fetched live from OpenAlex

This paper introduces a practical single-mode minimum deviation controller for indirect energy transfer converters. It provides theoretically minimum possible output voltage deviation during load transients. The controller has an outer voltage loop, providing half-duty ratio signals, that is triggered by an inner current loop. This controller behaves the same way in steady state and during transients. The effectiveness of the controller is verified experimentally, with a 5 V to 8 V, 16 W, 250 kHz boost converter prototype. The results demonstrate virtually minimal output voltage deviation. In comparison with a PID compensator that has a relatively high bandwidth of one-tenth of the switching frequency, the presented controller has about 2 times smaller deviation allowing for an equivalent output capacitor reduction.

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)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.192
Teacher spread0.183 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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