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Record W2768452929 · doi:10.1109/ecce.2017.8095786

Dual-loop controller for LLC resonant converters using an average equivalent circuit

2017· article· en· W2768452929 on OpenAlexaff
Franco Degioanni, Ignacio Galiano Zurbriggen, Martin Ordonez

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsConvertersControl theory (sociology)Transfer functionController (irrigation)Resonant converterLoop (graph theory)Operating pointSmall-signal modelPower (physics)Equivalent circuitElectronic engineeringPole–zero plotDual loopMaximum power transfer theoremDual (grammatical number)Point (geometry)RLC circuitComputer scienceEngineeringCapacitorPhysicsVoltageControl (management)MathematicsElectrical engineering

Abstract

fetched live from OpenAlex

LLC resonant converters are widely employed in industrial applications due to their high efficiency and power density. The design of closed-loop controllers for these converters is particularly challenging due to its non-linear behaviour and many different operating modes. The traditional linear techniques that are usually employed for this application require complex analysis or the use of empirical methods to obtain the small signal transfer functions of the system. This paper proposes a method to design a dual-loop controller using a simplified average model of the LLC resonant converter at resonant frequency. It is shown that the resonant condition represents the worst-case scenario from the control point of view, and that can be used as a reference point for designing the compensators. The theoretical analysis is validated by simulation and experimental results using a 150 W LLC resonant 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 score0.977
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 integrity0.0000.000
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 head0.066
GPT teacher head0.292
Teacher spread0.226 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

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