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Record W2913040985 · doi:10.1109/tie.2018.2890486

Displacement and Force Self-Sensing Technique for Piezoelectric Actuators Using a Nonlinear Constitutive Model

2019· article· en· W2913040985 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsUniversity of British Columbia, Okanagan CampusKelowna General HospitalUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDisplacement (psychology)Nonlinear systemHysteresisActuatorControl theory (sociology)PiezoelectricityCapacitanceVoltagePhysicsEngineeringAcousticsComputer science

Abstract

fetched live from OpenAlex

This paper describes a new self-sensing technique for piezoelectric actuators that estimates both displacement and force from voltage and charge measurements. The novelty of this technique lies in the use of a constitutive model with nonlinear capacitance and an exponential hysteresis model. The advantage of the presented self-sensing technique over similar techniques in the literature is that it only requires one parameter identification experiment and its hysteresis model is computationally efficient. The control performance of the self-sensing technique is evaluated in a sensorless displacement and force control structure. Validation experiments are carried out on a two degree of freedom test setup that mimics micropositioners by generating simultaneously varying displacements and forces. Less than 3% absolute average errors are obtained for both displacement and force.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.944
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.014
GPT teacher head0.223
Teacher spread0.209 · 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