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Record W2126655942 · doi:10.1115/1.4028055

A Survey of Modeling and Control Issues for Piezo-electric Actuators

2014· article· en· W2126655942 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

VenueJournal of Dynamic Systems Measurement and Control · 2014
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsActuatorHysteresisStiffnessControl (management)Control engineeringControl theory (sociology)VibrationCreepComputer scienceVibration controlEngineeringMechanical engineeringMaterials scienceAcousticsPhysicsStructural engineeringElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Piezo-electric actuators (PEAs) have been widely used in nanopositioning applications due to their high stiffness, fast responses, and large actuating forces. However, the existence of nonlinearities such as hysteresis can greatly deteriorate their performance and, as such, modeling and control of PEAs for improved performance has drawn considerable attention in the literature. This paper presents a brief survey of recent achievements in modeling and control of PEAs as well as the relevant issues that remain to be resolved. Specifically, various methods for modeling hysteresis, creep, and vibration dynamics in PEAs are examined, followed by a discussion of the issues leading to modeling errors. Recently reported PEA control schemes are surveyed along with their advantages and disadvantages. The challenges associated with control problems are also discussed.

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)0.004
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 score0.950
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.012
GPT teacher head0.208
Teacher spread0.196 · 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