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Record W2024703571 · doi:10.1115/dscc2014-6337

Robust Stall Flutter Suppression Using ℋ2/ℋ∞ Control

2014· article· en· W2024703571 on OpenAlexaff
Sohrab Haghighat, Zhiwei Sun, Hugh H. T. Liu, Junqiang Bai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsControl theory (sociology)FlutterAeroelasticityAerodynamicsNonlinear systemStall (fluid mechanics)Robustness (evolution)InflowRobust controlComputer scienceControl systemFrequency domainController (irrigation)EngineeringControl engineeringControl (management)

Abstract

fetched live from OpenAlex

Following the current trend in aeroelastic optimization, as wing structures have been made more flexible, active control systems such as flutter suppression systems have been widely adopted to reduce undesirable aeroelastic behaviors. The stability and the performance of flutter suppression control systems can be negatively affected as the inflow speed deviates from the nominal design value. In this work, a mixed-norm robust controller is designed to perform stall flutter suppression. A 2-dimensional nonlinear time-domain aeroservoelastic model is developed. The nonlinear equations are linearized at different flight conditions and are employed to construct an uncertainty model, which affects the nominal dynamics in an affine way. The obtained uncertain model of the aeroservoelastic system is used to design a mixed-norm H2/H∞ controller. The performance of the designed controller is compared with the performance of a non-robust H2 controller at different flight conditions. The proposed control architecture reduces the adverse effect of inflow speed variation on the performance of the closed-loop system.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.931
Threshold uncertainty score0.791

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)0.0010.000

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 head0.017
GPT teacher head0.192
Teacher spread0.175 · 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
Published2014
Admission routes1
Has abstractyes

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