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Record W4254371460 · doi:10.1260/0263-0923.33.4.469

Model Predictive Control of Vibration in a Two Flexible Link Manipulator — Part 2

2014· article· en· W4254371460 on OpenAlexaff
J.R. Elliott, Rickey Dubay, Atef Mohany, Marwan Hassan

Bibliographic record

VenueJournal of low frequency noise, vibration and active control · 2014
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsUniversity of GuelphOntario Tech UniversityUniversity of New Brunswick
Fundersnot available
KeywordsControl theory (sociology)BacklashVibrationModel predictive controlFinite element methodLink (geometry)Vibration controlPlanarComputer scienceController (irrigation)Matrix (chemical analysis)Structural engineeringEngineeringControl (management)PhysicsArtificial intelligenceAcousticsMaterials science

Abstract

fetched live from OpenAlex

A finite element based model predictive controller (FEMPC) is developed and practically implemented for attenuating in-plane vibration of a two flexible link planar manipulator. This FEMPC structure is based on that used in dynamic matrix control (DMC), with the exception that a finite element (FE) model replaces how the predictions are formulated. A linear FE model is developed for each individual link, which is used with the current measured strain and control actions, to predict the response of each link. These predictions are carried out at each time step to address the geometric non-linearities associated with the orientation of the second link and those associated with friction, backlash and compliance of the geared motors. Furthermore, the use of FE modelling enables the control structure to be formulated based on known properties of the system, eliminating the need for open loop testing. The resulting FEMPC scheme is shown to outperform DMC and is capable of providing substantial attenuation of vibration, reducing the mean amplitude of dominant vibration by 92.5% and 15.6%, for the first and second links, respectively.

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)0.001
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 score0.892
Threshold uncertainty score0.716

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 communication0.0000.001
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 head0.008
GPT teacher head0.219
Teacher spread0.210 · 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

Citations6
Published2014
Admission routes1
Has abstractyes

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