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Record W2989825955 · doi:10.1109/jestpe.2019.2956834

A Fast-Response RBAOT-Controlled Buck Converter With Pseudofixed Switching Frequency and Enhanced Output Accuracy

2019· article· en· W2989825955 on OpenAlexaff
Bing Yuan, Mengxue Liu, Wai Tung Ng, Xinquan Lai

Bibliographic record

VenueIEEE Journal of Emerging and Selected Topics in Power Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Toronto
FundersChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsRippleInductorVoltageBuck converterControl theory (sociology)Transient responseOffset (computer science)Overshoot (microwave communication)Electromagnetic interferenceComputer scienceElectrical engineeringElectronic engineeringEngineering

Abstract

fetched live from OpenAlex

A ripple-based constant on-time (RBCOT) control scheme is excellent in achieving fast transient response during the load current step. However, it suffers from severe electromagnetic interference (EMI) noise and inherent output voltage offset problem. A monolithic ripple-based adaptive on-time (RBAOT)-controlled buck converter is presented to overcome both drawbacks while retaining the advantage of RBCOT. The on-time is adjusted adaptively by detecting the switching node voltage, and thereby ensuring a pseudofixed switching frequency in a steady state, regardless of the input, output voltage, and load current conditions. A correction voltage generated by a coefficient in a virtual inductor current (VIC) circuit is introduced to counteract the offset voltage and enhance the output accuracy. The proposed scheme is simple to implement and suitable for high-conversion ratio applications. Experimental results show that the switching frequency is centered at 1 MHz with less than 6% variation for an output of 5 V and inputs ranging from 8 to 18 V. The output offset voltage is reduced from 16 to 1 mV for an 18 to 1.05-V conversion. For a load current step of 1 A, the output can be settled within <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6~\mu \text{s}$ </tex-math></inline-formula> and the undershoot and overshoot voltages are controlled to be within 20 mV.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.788
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.003
GPT teacher head0.211
Teacher spread0.208 · 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 designBench or experimental
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
Published2019
Admission routes1
Has abstractyes

Explore more

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