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Record W2896418187 · doi:10.1109/icma.2018.8484373

Neural Network-based Adaptive Sliding Mode Control for Gyroelastic Body

2018· article· en· W2896418187 on OpenAlexaff
Shiyuan Jia, Jinjun Shan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsYork University
Fundersnot available
KeywordsControl theory (sociology)Artificial neural networkVibrationController (irrigation)TorqueSliding mode controlAdaptive controlLyapunov stabilityMode (computer interface)Computer scienceLyapunov functionVibration controlAngular momentumStability (learning theory)PhysicsControl (management)Artificial intelligenceNonlinear systemAcousticsClassical mechanics

Abstract

fetched live from OpenAlex

Gyroelastic body refers to a flexible structure with a cluster of angular momentum devices. The torques exerted by the angular momentum devices can be used for active vibration suppression. This paper addresses the vibration suppression of gyroelastic body in the presence of uncertainties and external disturbances. A neural network-based adaptive sliding mode controller (NNASMC), combining neural network (NN) approximation, adaptive technique and sliding mode technique, is designed to ensure the vibration suppression of the gyroelastic body. The stability of the proposed NNASMC is proved using Lyapunov theory. Simulation results demonstrate the effectiveness of the proposed controller.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.517

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 head0.009
GPT teacher head0.211
Teacher spread0.201 · 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 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

Citations3
Published2018
Admission routes1
Has abstractyes

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