MétaCan
Menu
Back to cohort
Record W2110739662 · doi:10.1109/apec.2009.4802666

Self-Tuning Sensorless Digital Current-Mode Controller with Accurate Current Sharing for Multi-Phase DC-DC Converters

2009· article· en· W2110739662 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConvertersControl theory (sociology)EstimatorDuty cycleInductorBuck converterComputer scienceCurrent (fluid)Controller (irrigation)VoltageDigital controlPhase (matter)Electronic engineeringEngineeringPhysicsElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

A sensorless multi-phase average current-program mode controller with an accurate self-tuning current estimator is introduced. Based on the values of the duty ratio, and the input and output voltages, the estimator calculates the average inductor current of each phase in a multi-phase dc-dc converter. The averaged values are calculated over one switching cycle. The estimator employs a phase-by-phase self calibration scheme. During the calibration, regulators of all currents but one are "frozen" and a small load step is intentionally introduced, by a test current sink. Based on the response, the parameters of the active phase are automatically adjusted such that accurate estimation and equal current sharing are achieved. Experiments with a 12 V-to-1.5 V, 60 W, 500 kHz, two-phase buck converter verify that the controller ensures fast estimation and, at the full load, a current sharing error of less than 5%.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.030
GPT teacher head0.312
Teacher spread0.282 · 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

Quick stats

Citations35
Published2009
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced DC-DC ConvertersFrench-language works237,207