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Record W2002349061 · doi:10.1109/tie.2015.2412514

A Unified State-Space Model of Constant-Frequency Current-Mode-Controlled Power Converters in Continuous Conduction Mode

2015· article· en· W2002349061 on OpenAlexaff
Sean C. Smithson, Sheldon S. Williamson

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsConcordia University
Fundersnot available
KeywordsControl theory (sociology)ConvertersAdmittanceElectrical impedanceState spacePower (physics)Constant currentElectronic engineeringCurrent (fluid)Computer scienceEngineeringVoltageControl (management)PhysicsMathematicsElectrical engineering

Abstract

fetched live from OpenAlex

While current-mode control of dc-dc converters provides numerous benefits over voltage-mode control, previous models of the control strategy have been limited to the form of classical control models. Through the use of a state-space averaged (SSA) model of current-mode control, both the open-loop and closed-loop circuit parameters of open-loop control-to-output frequency response, conducted susceptibility, input admittance, output impedance, and any other desired response can be easily obtained. In this paper, SSA models of current-mode-controlled converters are derived and presented for buck, boost, and flyback topologies operating in continuous conduction mode. The new model allows for simpler and more accurate modeling than possible with previous methods, facilitates the modeling of cascaded converters, and allows for the use of state-variable feedback and other modern control methods in applications that use current-mode control.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.034
GPT teacher head0.260
Teacher spread0.226 · 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
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

Citations46
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Industrial ElectronicsSame topicAdvanced DC-DC ConvertersFrench-language works237,207