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Record W1553531217 · doi:10.1109/apec.2015.7104380

Dual-loop geometric-based control of buck converters

2015· article· en· W1553531217 on OpenAlexaff
Ignacio Galiano Zurbriggen, Matias Anun, Martin Ordonez

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsControl theory (sociology)Dual loopOvershoot (microwave communication)ConvertersPulse-width modulationTransient responseComputer scienceOperating pointBandwidth (computing)Controller (irrigation)Buck converterVoltageLoop (graph theory)EngineeringMathematicsElectronic engineeringControl (management)

Abstract

fetched live from OpenAlex

Voltage mode and dual-loop current-mode linear controllers are widely used in buck converters due to their simple implementation and fixed frequency PWM operation. While the dynamic response can be improved by pushing the control bandwidth, lower stability margins may lead to unexpected peak deviations, leading to failures due to magnetic saturations or excessive overshoot. A dual-loop geometric compensator is introduced in this work by combining state-plane analysis with traditional linear controllers. The proposed geometric voltage loop tightly controls the time-domain evolution of the state variables, providing a reliable transient response by following a desired geometrical path to reach the target steady state operating point. Straight line and circular trajectories are implemented resulting in an outstanding, well defined, and reliable transient behaviour. Experimental results of dual-loop geometric-based controlled 50W platform validate the proposed control concept and highlight the strong contribution to the applied field made by this innovative controller.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.597

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 communication0.0000.000
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 head0.014
GPT teacher head0.209
Teacher spread0.195 · 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 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

Citations7
Published2015
Admission routes1
Has abstractyes

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