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Record W2077784548 · doi:10.1109/tia.2013.2242032

Predictable Auxiliary Switching Strategy to Improve Unloading Transient Response Performance for DC–DC Buck Converter

2013· article· en· W2077784548 on OpenAlexaff
Liang Jia, Zhiyuan Hu, Yan‐Fei Liu, Paresh C. Sen

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsOvershoot (microwave communication)Settling timeBuck converterControl theory (sociology)Transient responseTransient (computer programming)Power (physics)VoltageComputer scienceController (irrigation)Ćuk converterPulse-width modulationBoost converterEngineeringStep responseElectrical engineeringControl (management)Control engineeringPhysics

Abstract

fetched live from OpenAlex

In this paper, a novel control strategy is presented, which is capable of controlling a 12-V-1.5-V main buck converter and an auxiliary circuit to achieve significantly improved unloading response performance. While the charge balance controller minimizes the settling time of the main buck converter, the auxiliary circuit is controlled in boundary conduction mode (BCM) for a predictable pattern of auxiliary switching to reduce the output overshoot. Therefore, the reliability and dynamic performance of the entire system is significantly enhanced. Compared with existing technologies, the proposed BCM auxiliary switching strategy achieves improved output voltage overshoot and reduced auxiliary power losses at the same time. Furthermore, numerical analysis of the improved output voltage overshoot and reduced auxiliary power losses has been conducted for a design guideline. Finally, simulation and experimental results are provided to verify the proposed scheme on a 12-V-1.5-V 10-A buck converter prototype.

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 categoriesMeta-epidemiology (narrow)
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 score0.840
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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.

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

Citations17
Published2013
Admission routes1
Has abstractyes

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