MétaCan
Menu
Back to cohort
Record W2323037904 · doi:10.2514/6.2012-1480

Advances in Helicopter Vibration Control Methods Time-Periodic Reduced Order Modeling and H2/H-Infinity Controller Design

2012· article· en· W2323037904 on OpenAlexaff
Fatma Ülker, Fred Nitzsche

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicModel Reduction and Neural Networks
Canadian institutionsCarleton University
Fundersnot available
KeywordsControl theory (sociology)H-infinity methods in control theoryVibrationController (irrigation)Vibration controlInfinityComputer scienceControl engineeringControl (management)MathematicsEngineeringPhysicsMathematical analysisAcoustics

Abstract

fetched live from OpenAlex

This paper presents the implementation of recent developments in system theory within a novel framework to enhance the vibration control of helicopters. Particular focus is given to the vibration control of helicopters ying in a forward ight regime, where the system exhibits time-periodic behavior. The objective of this framework is to provide high performance controllers that can satisfy stability and design performance criteria when implemented in high-delity computer simulations or in real time experiments. The framework emphasizes the integration of state-of-the art coupled Computational Fluid Dynamics (CFD) /Computational Structural Dynamics (CSD) analysis in the controller design process to obtain accurate reduced-order aeroelastic models of the helicopter rotor system. Design of time-periodic H2 and H1 controllers are proposed owing to their rigorous stability formulation based on Floquet-Lyapunov theory, and advantages over time-lifted controllers. Within this framework, the time-periodic system models in state-space form were identied using robust subspace model identication method. The time-periodic H2 and H1 synthesis problem was solved using both Linear Matrix Inequality and periodic Riccati based formulations. The controllers performance were validated using the high-delity aeroelastic simulations. The computational eciency of using these advanced methods, and the necessity of using the novel framework were demonstrated by implementing an actively controlled ap strategy for vibration suppression of helicopters.

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: none
Teacher disagreement score0.936
Threshold uncertainty score0.861

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.313
Teacher spread0.286 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicModel Reduction and Neural NetworksFrench-language works237,207