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Record W2144683695 · doi:10.1109/iecon.2013.6699388

Load current control of a boost converter using output redefinition

2013· article· en· W2144683695 on OpenAlexaff
Yaser M. Roshan, Mehrdad Moallem

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsControl theory (sociology)Duty cycleBoost converterController (irrigation)Nonlinear systemCurrent (fluid)Computer scienceFeedback linearizationNonlinear controlInductorVoltageControl (management)EngineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Control of the output current of a boost type DC-DC converter is challenging due to the non-minimum phase characteristic between the duty cycle input and output load current and the nonlinear dynamics involved. To address the control challenges, this paper presents a nonlinear control scheme combined with an output redefinition approach to regulate the output current of the converter operating in the continuous conduction mode (CCM). To this end, a feedback linearization controller is proposed based on an averaged model of the converter. The output redefinition concept relies on defining a new output to make the system minimum phase, or marginally minimum phase, so that a robust controller can be designed. Furthermore, control in the discontinuous conduction mode is studied and a switching scheme is presented to regulate the output current of the converter regardless of the operation mode. Numerical simulations are presented to evaluate performance of the proposed control scheme.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.221
Teacher spread0.202 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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