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Modeling and Identification of High Speed High Accuracy Lightweight Stages with Flexible Arms

2012· article· en· W2025450733 on OpenAlexfundno aff
Shixun Fan, Denggui Fan, Z.Y. Zhang, Yao Lu

Bibliographic record

VenueKey engineering materials · 2012
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsnot available
FundersChina Scholarship CouncilNational University of Defense TechnologyUniversity of British ColumbiaNational Natural Science Foundation of China
KeywordsVibrationControl theory (sociology)Identification (biology)Settling timeNonlinear systemRigid bodySystem identificationComputer scienceMotion (physics)Dynamics (music)EngineeringControl engineeringSimulationAcousticsStep responseControl (management)Artificial intelligencePhysics

Abstract

fetched live from OpenAlex

Light-weight structures operating at high speeds may suffer from significant vibration problems, thus degrading positioning accuracy and exhibiting large settling time. High-performance vibration and motion controllers are usually designed based on precise dynamic model. This paper addresses the problem of modeling and identification of a high speed high accuracy lightweight positioning stage with flexible arm. A simplified state space model for vibration motion control purpose is given. A separated identification method is proposed. The parameters related to rigid body part and flexible body part are identified, separately. Rigid body dynamics is firstly identified by employing unbiased Least Squares technique. Subsequently, flexible body dynamics is identified by impact test and nonlinear LS technique. The experiment results show that applying proposed identification strategy can get more precise low frequency rigid body dynamic parameters without loss the identification precision of high frequency parameters.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.803

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 head0.015
GPT teacher head0.251
Teacher spread0.236 · 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 designBench or experimental
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

Citations0
Published2012
Admission routes1
Has abstractyes

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