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Record W2111003315 · doi:10.2514/1.40158

Analysis and Simulation of Optimal Vibration Attenuation for Underactuated Mechanical Systems

2009· article· en· W2111003315 on OpenAlexaff
S. Woods, W. Szyszkowski

Bibliographic record

VenueAIAA Journal · 2009
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAttenuationVibrationUnderactuationControl theory (sociology)Aerospace engineeringMechanicsAcousticsStructural engineeringComputer sciencePhysicsEngineering

Abstract

fetched live from OpenAlex

This paper deals with active optimal vibration attenuation of elastic structures modeled by finite elements. The system’s equations are linear with potentially large numbers of degrees of freedom, whereas the minimized performance index isquadratic. Theproblem isformulated inmodal space so thatthe dimension ofthe problem can belimitedtocontrollingsignificantmodesonly.Theirnumberisconsideredgreaterthanthenumberofindependent discrete actuators, making the system underactuated. The constraints resulting from underactuation are representedbythematrixofconstraints thatcouplesthe modalcontrols. Thismatrix, whichplaysanimportant role in predicting the systems controllability, is obtained by adding a set of dummy actuators. Themodal variables are in turn coupled via second-order nonholonomic constraints, which are satisfied with the help of time-dependent Lagrange multipliers. The optimality equations for the problem are derived in a compact form and solved by applying symbolic differential operators. The procedure, which applies standard finite element and mathematical software, renders the optimal actuation forces and the response of all controlled modes, or any selected degrees of freedom, for the entire control process. Two simulation examples are presented to illustrate the approach’s details and the use of controllability indicators derived from the matrix of constraints.

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 score0.799
Threshold uncertainty score0.253

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 head0.010
GPT teacher head0.237
Teacher spread0.227 · 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
Published2009
Admission routes1
Has abstractyes

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