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CHATTERING REDUCTION IN A GEOMETRIC SLIDING MODE METHOD. A ROBUST LOW-CHATTERING CONTROLLER FOR AN AUTONOMOUS WIND SYSTEM

2009· article· en· W2041564950 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueControl and Intelligent Systems · 2009
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsControl theory (sociology)Reduction (mathematics)Mode (computer interface)Controller (irrigation)Computer scienceControl engineeringMathematicsArtificial intelligenceEngineeringGeometryControl (management)

Abstract

fetched live from OpenAlex

This paper focuses on the design of reduced chattering sliding mode controllers and its application to the optimization of a wind energy system. It develops, in an accessible way, an enhanced control design method suitable for a wide range of engineering applications, namely those that accept a certain degree of discontinuous action in their input and that can be described as non-linear systems affine in the control with a vector of bounds for the uncertainties. The proposed systematic method is based on a geometric approach developed by the authors in a previous work. This new proposal reduces the chattering while maintaining the attractive features of the original method, those are robustness, tuning simplicity and reaching mode control. Taking advantage of these attributes, a robust low-chattering controller for an autonomous wind energy conversion system based on a permanent magnet generator is developed.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.840
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.248
Teacher spread0.227 · 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