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Record W2274346621 · doi:10.1109/iecon.2015.7392313

A multi-surface sliding-mode extremum seeking controller for alternator maximum power point tracking

2015· article· en· W2274346621 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicExtremum Seeking Control Systems
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsControl theory (sociology)AlternatorRobustness (evolution)Maximum power point trackingMaximizationMaximum power principleParametric statisticsComputer scienceSliding mode controlController (irrigation)Operating pointLyapunov functionPower (physics)EngineeringMathematicsNonlinear systemVoltageMathematical optimizationElectronic engineeringPhysics

Abstract

fetched live from OpenAlex

Power maximization is of great interest in energy conversion systems including renewable sources and alternators used in automotive systems. This paper presents a multi-surface sliding-mode extremum seeking controller for achieving Maximum Power Point Tracking (MPPT) for energy conversion involving a Lundell alternator. The proposed multi-surface sliding scheme results in smooth and fast convergence to the extremum point while achieving robust performance in face of parametric uncertainties in the alternator model. In particular, it is shown that using multiple sliding surfaces would help to reduce chattering and increase the performance speed and precision of the system. The behavior of the system in terms of convergence characteristics is analyzed using a Lyapunov-like stability analysis. Simulation results demonstrate the efficiency of the proposed controller in terms of power maximization, robustness, and convergence speed.

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)0.001
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 score0.958
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.039
GPT teacher head0.270
Teacher spread0.231 · 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

Quick stats

Citations4
Published2015
Admission routes1
Has abstractyes

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