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Record W3192330224

Frequency domain system identification of fixed-wing unmanned aerial vehicles

2014· dissertation· en· W3192330224 on OpenAlexfundno aff
Kaiwen Xu

Bibliographic record

VenueMspace (University of Manitoba) · 2014
Typedissertation
Languageen
FieldEngineering
TopicControl Systems and Identification
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsFixed wingIdentification (biology)WingAeronauticsAerospace engineeringMarine engineeringComputer scienceGeographyEngineeringBiologyEcology
DOInot available

Abstract

fetched live from OpenAlex

The goal of this thesis is to identify airplanes’ reduced order transfer functions, and aerodynamic derivatives in the longitudinal channel. The outcome of the research will benefit aircraft systems’ controller design, modeling and simulation. To identify the system transfer functions and aerodynamic derivatives, direct and indirect frequency domain identification methods are applied. For the direct method, the Equation Error (EE) method is adopted to process the Cropcam’s input-output data pairs and identify the aerodynamic derivatives from the flight data directly. The indirect approach is called the Transfer Function (TF) method. The derivatives identified by the EE method and transfer function method are compared with the ones computed from a Vortex Lattice based program called AVL. The identification results are further verified by comparing computer simulation outputs with flight test responses.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 head0.006
GPT teacher head0.172
Teacher spread0.166 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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