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Record W2004051523 · doi:10.1049/iet-cta.2010.0609

Robust adaptive control of a one degree of freedom electrostatic microelectromechanical systems model with output-error-constrained tracking

2011· article· en· W2004051523 on OpenAlexaff
Weijie Sun, John T. W. Yeow, Zhendong Sun

Bibliographic record

VenueIET Control Theory and Applications · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsControl theory (sociology)Tracking errorLyapunov functionActuatorController (irrigation)Position (finance)Displacement (psychology)Adaptive controlTracking (education)Constraint (computer-aided design)Computer scienceMathematicsControl (management)Nonlinear systemPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

This study addresses an output-error-constrained tracking problem for the one degree of freedom parallel-plate electrostatic actuator. The authors first formulate the control problem as a robust output regulation problem for an output feedback system. Then they show that it can be further converted into a robust regulation problem with output constraint by internal model design. Finally, a regulation controller for this regulation problem by using a barrier Lyapunov function technique is designed. By an appropriate selection of some controller parameter and an appropriate initial displacement of the movable plate, the final designed output-error-constrained tracking control law ensures that, in the presence of large parameter variations, the harmonic displacement of the parallel-plate electrostatic actuator can be beyond the pull-in position and up to the full gap without contacts between the movable and fixed plates during the transient period.

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)0.001
metaresearch head score (Gemma)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.036
GPT teacher head0.209
Teacher spread0.173 · 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

Citations26
Published2011
Admission routes1
Has abstractyes

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