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Record W2122379347 · doi:10.1109/iciea.2008.4582908

Modeling and simulation of Parallel Current Mode controlled boost converter

2008· article· en· W2122379347 on OpenAlexaff
Xiaodong Liu, Pengyi Yang, Yan-Fei Liu, Jiaojiao Deng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsInductorMATLABVoltageControl theory (sociology)Current (fluid)Computer scienceBoost converterSIGNAL (programming language)Electronic engineeringSmall-signal modelMode (computer interface)EngineeringElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

In this paper, Parallel Current Mode Control method for Boost DC-DC converter is proposed. The parallel current mode control strategy is composed of two parallel terms: one is voltage term, the other is current term. They are calculated based on the input voltage, reference output voltage, inductor current and reference inductor current. The Parallel Current Mode Control method essentially distinguishes itself from the conventional current mode control method with two regulators, one for voltage regulation and the other for current regulation. Under the conventional Voltage Mode Control method and the new control method, small-signal models of Boost DC-DC converter are derived. The simulation result (based on the transfer functions) using Matlab/Simulink is compared with the simulation result based on the circuit simulation model, using PSIM. Results both in Matlab/Simulink environment and in PSIM environment are in good agreement, confirming the validity of the small-signal model. Both the small-signal analysis and simulation results demonstrate that Parallel Current Mode Control has superior performance as compared with Voltage 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 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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.851
Threshold uncertainty score0.402

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 head0.020
GPT teacher head0.253
Teacher spread0.234 · 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

Citations8
Published2008
Admission routes1
Has abstractyes

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