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Record W2134087845 · doi:10.1109/acc.2008.4586901

Feedforward control of a piezoelectric flexure stage for AFM

2008· article· en· W2134087845 on OpenAlexafffund
Yang Li, John Bechhoefer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFeed forwardTransfer functionBandwidth (computing)Computer scienceControl theory (sociology)SIGNAL (programming language)Control systemIterative learning controlFilter (signal processing)PiezoelectricityControl engineeringElectronic engineeringControl (management)EngineeringArtificial intelligenceComputer visionTelecommunications

Abstract

fetched live from OpenAlex

We review basic issues in the control of scanning probe microscopes. To improve the performance of the present generation of instruments, we have developed a simple feedforward technique that nonetheless increases the effective bandwidth of the positioning stage by a factor of 15 over its standard operation. If the desired control signal is known in advance (as it is for a periodic scan signal), the feedforward filter can be non-causal: information about the future can be used to cancel the phase lag produced by the stage response. We compare our design with other control techniques. We show that model-based iterative control algorithms can lead to a substantial performance boost, at the cost of more measurements of the system transfer function. We then introduce a model-free variant that is simpler to set up, performs better, and is more robust to system changes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.189
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), 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

Citations24
Published2008
Admission routes2
Has abstractyes

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