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Record W2169540778 · doi:10.1109/pacrim.1997.620330

Active shape control of a deformable mirror in an adaptive optics system

2002· article· en· W2169540778 on OpenAlexaff
A. Chellabi, Y. Stepanenko, S. Dost

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsControl theory (sociology)Controller (irrigation)TrajectoryDeformable mirrorSurface (topology)Optimal controlAdaptive controlComputer scienceControl systemSliding mode controlActuatorMathematicsControl (management)Nonlinear systemPhysicsEngineeringGeometryMathematical optimizationArtificial intelligence

Abstract

fetched live from OpenAlex

A new control methodology for active shape control of dynamic flexible structures is presented. This method is based on the direct optimal control approach developed for discrete systems. In this method, the algebraic equations of motion of a flexible structure are derived and used for the controller design. The algebraic optimal control laws are derived in an explicit form for a general time varying distributed parameter system with piezoelectric inclusions. The essence of the approach is based on using space-time assumed mode expansions of the generalized coordinates and inputs, which is a Rayleigh-Ritz method extended to both space and time dimensions in conjunction with variational work-energy principles that govern the physical system. An optimal tracking controller was designed for shaping the surface of the deformable mirror used in an adaptive optics system. The desired surface trajectory (over time) can be supplied analytically. This data is curve fitted for direct use. One feature of this method is that it accommodates the dynamic behavior of the structure instead of considering only the static shape control.

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: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.507

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 head0.025
GPT teacher head0.229
Teacher spread0.205 · 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
Published2002
Admission routes1
Has abstractyes

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