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Record W2124349550 · doi:10.1177/1077546308091215

A Procedure for Solving Under-actuated Optimal Attenuation of Vibrations by means of Symbolic Operators

2009· article· en· W2124349550 on OpenAlexaff
W. Szyszkowski, Nitin Ratnakar Dhotre

Bibliographic record

VenueJournal of Vibration and Control · 2009
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsLagrange multiplierControllabilityControl theory (sociology)ActuatorMathematicsBoundary value problemOptimal controlAttenuationHolonomicMathematical optimizationApplied mathematicsMathematical analysisComputer scienceControl (management)Physics

Abstract

fetched live from OpenAlex

The problem of optimal attenuation of vibrations with the number of controlled modes greater than the number of independent discrete actuators is analyzed. The modal variables are coupled via non-holonomic constraints that are satisfied with the help of time-dependent Lagrange's multipliers. A given configuration of actuators is characterized by a normalized matrix of constraints with constant coefficients. The coefficients are used to evaluate controllability and overall effectiveness of the actuators as configured. For the quadratic performance index the optimality equations are derived from Pontryagin's principle in a compact form containing time derivatives of the controlled modal variables and Lagrange's multipliers. The problem is formulated as a two-points-boundary-value problem for the modes involved. The equations are solved automatically by applying symbolic differential operators and standard mathematical software. The proposed procedure consists of structural and control phases, and permits determining the optimal histograms of forces in each actuator and the expected response of the structure, including the active damping ratios. It also provides hints for adjusting the configuration of actuators to improve their performance in the case where some of the modes are too difficult to control. Numerical examples illustrate the procedure.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.912
Threshold uncertainty score0.295

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 head0.005
GPT teacher head0.210
Teacher spread0.205 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

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