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Record W4319779146 · doi:10.1115/imece2022-96406

Vibration Control of a Cantilever Beam Using Reduced Model

2022· article· en· W4319779146 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueVolume 5: Dynamics, Vibration, and Control · 2022
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCantileverVibrationFinite element methodBeam (structure)Control theory (sociology)Reduction (mathematics)Vibration controlRealization (probability)Linear-quadratic regulatorEigenvalues and eigenvectorsOptimal controlComputer scienceEngineeringStructural engineeringMathematicsPhysicsAcousticsMathematical optimizationControl (management)Geometry

Abstract

fetched live from OpenAlex

Abstract One of objectives of the research work here is to actively control vibration of a flexible manipulator’s end-effector. Another objective is to reduce computational cost to control vibration. When the manipulator is in lock configuration, it can be considered as a cantilever beam with base motion. In this paper, finite-element analysis (FEA) is used to obtain natural frequencies (eigenvalues) and mode shapes (eigenvectors) for a cantilever beam. The eigenvectors are translated into uncoupled state space equations, based on balanced realization and Match-DC-Gain model reduction algorithm. Linear quadratic regulator is employed for full and reduced models of the beam. Contribution of this research is on verifying analytical approach (reduced model) with numerical model (FEA). Contribution also includes computational efficiency of the model reduction. Simulation results for vibration suppression of the cantilever beam are obtained and compared with two different models.

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)0.000
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 score0.764
Threshold uncertainty score0.800

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 head0.006
GPT teacher head0.182
Teacher spread0.176 · 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