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Record W2119601550 · doi:10.1109/ecce.2009.5316138

Oversampled digital power controller with bumpless transition between sampling frequencies

2009· article· en· W2119601550 on OpenAlexaff
Simon Effler, Zdravko Lukić, Aleksandar Prodić

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOversamplingLatency (audio)ConvertersDigital controlControl theory (sociology)Sampling (signal processing)Electronic engineeringComputer scienceAutomatic frequency controlSwitching frequencyPower (physics)EngineeringController (irrigation)Control (management)Filter (signal processing)Electrical engineeringCMOSTelecommunications

Abstract

fetched live from OpenAlex

In today's digitally controlled power supplies fast analog-digital converters sampling at a multiple of the switching frequency are used to reduce the latency time of the conversion. Conversely, in many cases the actual compensator is still sampled at the switching frequency which introduces an additional latency time. To reduce this latency time, a new compensator architecture is presented in this paper which allows a bumpless transition between two compensators operating at two different sampling frequencies. Operating at the switching frequency during steady-state provides noise suppression, while operating at the full “oversampling” frequency during transients reduces the compensator's latency time significantly. A method for the bumpless transition between the two compensators is presented which is simple to implement and can be easily integrated into existing control architectures. Experimental verification demonstrates clear performance gain over existing control architectures.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.895
Threshold uncertainty score0.630

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 integrity0.0000.000
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 head0.011
GPT teacher head0.207
Teacher spread0.196 · 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 designOther design
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

Citations24
Published2009
Admission routes1
Has abstractyes

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