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Record W2048088018 · doi:10.1088/0964-1726/14/4/044

Study of system parameters and control design for a flexible manipulator using piezoelectric transducers

2005· article· en· W2048088018 on OpenAlexafffund
Mehrdad R. Kermani, Mehrdad Moallem, Rajni V. Patel

Bibliographic record

VenueSmart Materials and Structures · 2005
Typearticle
Languageen
FieldEngineering
TopicDynamics and Control of Mechanical Systems
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPiezoelectricityTransducerManipulator (device)Control engineeringControl theory (sociology)EngineeringComputer scienceAcousticsControl (management)Mechanical engineeringPhysicsElectrical engineeringRobotic armArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, a nonlinear control scheme is presented to achieve small tracking errors in a 2-DOF flexible manipulator.A secondary actuation mechanism using piezoelectric materials is added to the system for suppressing residual vibrations at the end point of the flexible link.A small piece of piezoceramic is also used, as a sensor, in order to obtain the modal states of the system.The effects of changing physical parameters such as relative thickness of the piezoelectric ceramic with respect to the flexible link, the optimum location and the length of the actuator are studied based on the singular value decomposition of the controllability Grammian of the system.It is shown that for each of the aforementioned parameters, an optimum value can be found which maximizes the singular value associated with one vibration mode.A partial feedback linearization technique based on output redefinition is utilized to obtain an appropriate control output for each joint and the piezoelectric actuator.A model for friction is obtained and included in the control law.Experimental results show that applying the suggested control scheme results in smooth and precise motion of the flexible manipulator without exciting unwanted vibration modes.Comparisons are made when a linear control scheme is used for the tracking problem.

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: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.486

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 head0.020
GPT teacher head0.222
Teacher spread0.202 · 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

Citations9
Published2005
Admission routes2
Has abstractyes

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