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Record W2086947436 · doi:10.1109/isie.2014.6864625

Feedback/feedforward control of hysteresis-compensated piezoactuators for highspeed scanning applications

2014· article· en· W2086947436 on OpenAlexaff
Yanfang Liu, Jinjun Shan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsYork University
Fundersnot available
KeywordsFeed forwardControl theory (sociology)ActuatorTrajectoryCompensation (psychology)Tracking (education)CapacitorController (irrigation)Computer scienceNonlinear systemHysteresisFeedforward neural networkControl engineeringEngineeringControl (management)PhysicsVoltageArtificial neural network

Abstract

fetched live from OpenAlex

This paper presents a control scheme for piezoelectric actuators (PEAs) in high-speed triangular trajectory scanning applications. Nonlinear hysteresis is compensated using a Maxwell resistive capacitor model. Based on the frequency response of the identified linear model, the feedforward compensation is proposed. This feedforward strategy is then combined with a proportional plus integral (PI) feedback controller for high-speed triangular trajectory tracking using piezoelectric actuator. Fast and precise tracking results are demonstrated through experiments. Moreover, it is found that the feedback loop does not always improve the tracking accuracy. When the input frequency exceeds a certain value, feedforward control only will result in better control performance.

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: none
Teacher disagreement score0.977
Threshold uncertainty score0.730

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 head0.004
GPT teacher head0.189
Teacher spread0.185 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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