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Record W1757494804

Compensation of a piezoceramic actuator hysteresis nonlinearities using the stop operator-based Prandtl-Ishlinskii model

2010· article· en· W1757494804 on OpenAlexaff
Omar Aljanaideh, Subhash Rakheja, Chun‐Yi Su

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsConcordia University
Fundersnot available
KeywordsControl theory (sociology)HysteresisFeed forwardActuatorNonlinear systemPrandtl numberCompensation (psychology)Operator (biology)Computer sciencePhysicsEngineeringControl engineeringMechanicsControl (management)ConvectionArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

In this study, the complementary property of the stop operator-based Prandtl-Ishlinskii model (SPI) in relation to the play operator-based Prandtl-Ishlinskii model (PPI) is explored in order to suppress the hysteresis nonlinearity of a piezoceramic micro-positioning actuator. The SPI compensator is constructed on the basis of the play operator-based Prandtl-Ishlinskii model (PPI) used to characterize the hysteresis of the actuator. The simulation results are obtained to illustrate the complementary properties of the SPI and PPI models in terms of the shape and direction of their hysteresis loops. The implementation of the SPI model as a feedforward compensator in conjunction with the PPI model resulted in effective suppression of the major as well as minor loops hysteresis, particularly when the SPI model was identified on the basis of known hysteresis nonlinearity. Laboratory experiments performed with a piezoceramic micro-positioning stage using hardware-in-the-loop test method together with the proposed SPI model compensator also confirmed effective suppression of the major as well as minor loops hysteresis.

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 score0.556
Threshold uncertainty score0.467

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 head0.011
GPT teacher head0.218
Teacher spread0.207 · 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
Published2010
Admission routes1
Has abstractyes

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