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Record W2914293447 · doi:10.1088/1361-665x/ab028b

A two-variable extremum seeking controller with application to self-tuned vibration energy harvesting <sup>∗</sup>

2019· article· en· W2914293447 on OpenAlexafffund
Seyed Hossein Kamali, Mehrdad Moallem, Siamak Arzanpour

Bibliographic record

VenueSmart Materials and Structures · 2019
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVibrationControl theory (sociology)Controller (irrigation)Energy harvestingVariable (mathematics)Energy (signal processing)EngineeringComputer scienceMathematicsAcousticsPhysicsControl (management)Mathematical analysisArtificial intelligenceStatistics

Abstract

fetched live from OpenAlex

Abstract A new control strategy for sliding-mode extremum seeking in two-variables is proposed in this study. The controller converts the two-variable search problem into a single-variable problem in which the direction of changes on the two-dimensional phase plane is used as the sliding mode variable. Simulations are conducted to show that the proposed method is simpler, faster and more accurate than multi-variable sliding mode extremum seeking control methods in the current literature. The proposed controller is utilized for maximum power point tracking in vibration energy harvesters where both damping and stiffness of the harvester can be tuned using power electronic techniques. Experimental results show that the proposed controller is capable of tuning the variables for seeking the extremum point without using any model of the system.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.797

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 head0.004
GPT teacher head0.184
Teacher spread0.180 · 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 designBench or experimental
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

Citations10
Published2019
Admission routes2
Has abstractyes

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