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On Precision Motion Control for an Industrial Long-Stroke Motion System with a Nonlinear Micropositioning Actuator

2024· article· en· W4402288845 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsActuatorMotion (physics)Control theory (sociology)Nonlinear systemMotion controlPneumatic actuatorStroke (engine)Control engineeringComputer scienceControl (management)EngineeringArtificial intelligencePhysicsMechanical engineeringRobot

Abstract

fetched live from OpenAlex

In this study, we propose a control technique that can be used to significantly reduce the tracking error of a short-stroke motion system whose dynamics are considered totally unknown with possibly input and output nonlinearities. As an example, the tracking performance of a uni-axial piezoceramic actuated positioning stage is examined under mainly sinusoidal test input signals with various frequencies and amplitudes, where we show that the proposed controller does invert the plant dynamics. Interestingly, neither modeling nor identification of the stage dynamics is needed. Mainly, the indented operation bandwidth is the only needed information when the proposed controller is synthesized. Experimental results demonstrate the effectiveness of the proposed approach when the piezoceramic actuated stage is attached to an existing long-stroke positioning motion system as an add-on to motivate the former usefulness in specifically enhancing the performance of wafer scanner machines used in semiconductor manufacturing.

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 categoriesnone
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 score0.785
Threshold uncertainty score0.748

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 head0.227
Teacher spread0.216 · 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

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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