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Record W2095862724 · doi:10.1109/apec.2010.5433362

Multiphase optimal response mixed-signal current-programmed mode controller

2010· article· en· W2095862724 on OpenAlexaff
Jurgen Alico, Aleksandar Prodić

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsControl theory (sociology)CapacitorController (irrigation)Transient (computer programming)Transient responseSteady state (chemistry)Computer scienceVoltageBuck converterCurrent (fluid)Power (physics)Digital controlSIGNAL (programming language)Electronic engineeringEngineeringElectrical engineeringPhysicsControl (management)

Abstract

fetched live from OpenAlex

This paper presents a simple and practical implementation of a proximity optimal-time (OT) response controller for multiphase interleaved dc-dc switch-mode power supplies (SMPS). This novel solution enables equal current sharing between phases not only in steady state, but also during load transients. It also achieves a bump-less transition between the transient and steady state, where the interleaved operation is resumed without any delay. To minimize calculation burden and hardware complexity, a single digital voltage loop and multiple analog current loops are combined to implement a capacitor-charge balance based optimal-time recovery algorithm. The interface between the loops is provided through a structure consisting of a sample-and-hold circuit (S&H) and a relatively slow successive approximation digital-to-analog converter (DAC) providing control signals for all the current loops. The effectiveness of the controller is demonstrated on a 2-phase, 5 V-to-1.8 V, 20 W, interleaved buck converter operating at a 1 MHz switching frequency. The experimental results verify equal current sharing under all operating conditions, bump-less transition between the modes, and demonstrate that upon a transient converter reaches new steady state in the virtually fastest possible time.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.947
Threshold uncertainty score0.902

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 head0.008
GPT teacher head0.254
Teacher spread0.247 · 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

Citations34
Published2010
Admission routes1
Has abstractyes

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