MétaCan
Menu
Back to cohort
Record W2086374747 · doi:10.1002/asjc.146

Nonlinear control of power converters: a new adaptive backstepping approach

2009· article· en· W2086374747 on OpenAlexaff
Jun Fu, Ying Jin, Jun Zhao

Bibliographic record

VenueAsian Journal of Control · 2009
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsBacksteppingConvertersControl theory (sociology)Nonlinear systemControl engineeringControl (management)Computer sciencePower (physics)Adaptation (eye)Adaptive controlEngineeringArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

Abstract This paper proposes a new backstepping approach to nonlinear control of power converters which is attracting considerable attention in both theoretical research and practical applications. The main difference between the proposed algorithm and the existing classical adaptive backstepping method in the literature is that the adaptation mechanism does not follow the certainty‐equivalence principle. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.004
GPT teacher head0.176
Teacher spread0.172 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueAsian Journal of ControlSame topicMicrogrid Control and OptimizationFrench-language works237,207