MétaCan
Menu
Back to cohort
Record W2801309464 · doi:10.1139/tcsme-2017-1044

VIBRATION SUPPRESSION OF A ROTATING HUB-BEAM SYSTEM WITH A FLEXIBLE SUPPORT USING FRACTIONAL ORDER SLIDING MODE CONTROL

2017· article· en· W2801309464 on OpenAlexvenueno aff
Mohsen Vakilzadeh, Mohammad Eghtesad, Mohammad Rahim Nami, Ghasem Khajepour

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicMagnetic Bearings and Levitation Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsController (irrigation)Control theory (sociology)VibrationActuatorHelicopter rotorEngineeringMode (computer interface)Beam (structure)Sliding mode controlComputer scienceStructural engineeringNonlinear systemAcousticsPhysicsControl (management)

Abstract

fetched live from OpenAlex

In this paper, a rotating hub-blade system with a flexible support which represents a wide range of industrial applications is considered for modelling and control. The flexible blade is assumed as an Euler–Bernoulli beam. In addition, three piezoelectric layers are mounted on the blade as sensors and actuators to reduce vibrations of the blade attached to the hub. For modelling, the Lagrange’s method is utilized to obtain the equations of motion of the system. In order to simultaneously suppress vibrations of the system and track the desired angular position of the hub, designing an appropriate controller is carried out. In this regard, a fractional order sliding mode (FOSM) controller is proposed to fulfil these objectives and then the comparison between FOSM controller and the classical sliding mode controller is presented in order to investigate the effectiveness of the proposed controller. The simulation results indicate the superior performance of the fractional order controller in compare to the integer order sliding.

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: Methods · Consensus signal: none
Teacher disagreement score0.921
Threshold uncertainty score0.690

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 head0.012
GPT teacher head0.219
Teacher spread0.208 · 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
GenreMethods

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

Citations8
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicMagnetic Bearings and Levitation DynamicsFrench-language works237,207