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Record W2726410576 · doi:10.1049/iet-est.2017.0005

Non‐linear time‐delay controller for dc/dc power converters in application of electric vehicles

2017· article· en· W2726410576 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIET Electrical Systems in Transportation · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsPolytechnique Montréal
FundersIran National Science Foundation
KeywordsConvertersControl theory (sociology)Controller (irrigation)VoltageMATLABPower (physics)Context (archaeology)Electric power systemComputer scienceEngineeringElectronic engineeringElectrical engineeringControl (management)Physics

Abstract

fetched live from OpenAlex

dc/dc Power converters are increasingly used in vehicular power systems with their control systems re‐designed to overcome the challenges associated with the wide voltage and load variations and the non‐linear behaviour of constant power loads (CPLs). Within this context, this study investigates the implementation of a non‐linear time‐delay control (TDC) system in dc/dc power converters. This non‐linear controller compares the system response with the response of a reference model, and then generates a control signal which forces the system to follow the reference model. The TDC system is designed to tightly regulate the output voltage of a conventional dc/dc boost converter under input voltage and load variations. Furthermore, the stability of the TD controller following to the CPL variations is studied. To verify the effectiveness of TD controller, its performance is compared with an integral‐double‐lead controller using MATLAB software.

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.

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 score0.865
Threshold uncertainty score0.865

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 head0.005
GPT teacher head0.227
Teacher spread0.221 · 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