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Feedforward Control for Wind Rejection in Fixed-Wing UAVs

2022· article· en· W4288047676 on OpenAlexafffund
Jackson Empey, Meyer Nahon

Bibliographic record

Venue2022 International Conference on Unmanned Aircraft Systems (ICUAS) · 2022
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFeed forwardControl theory (sociology)Controller (irrigation)ThrustEngineeringComputer scienceControl engineeringAerospace engineeringControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

This paper presents a modular feedforward wind rejection method for agile fixed-wing UAVs, designed to work in tandem with a thrust pointing position controller. The feedforward controller requires a trustworthy wind estimate, and is based on three components, a feedforward rotation on attitude and two feedforward components on thrust. The controller uses attitude trim conditions to reject wind disturbances near the cross-wind condition, while also adopting an airspeed-based feedforward on thrust to reduce disturbances when flying in-line with the wind vector. The feedforward controller was validated in simulation and outdoor flight tests. Simulated flights consisted of straight line and circular tracking in a constant wind-field, as well as frequency tests to examine the ability of the feedforward controller to reject wind fluctuations. Experimental flight tests tracking a circular path in a windy environment were conduced. The results of simulations and experimental flights show that the feedforward controller provides good wind rejection performance when compared to the feedback position controller operating on its own.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.284
Threshold uncertainty score1.000

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 head0.017
GPT teacher head0.239
Teacher spread0.222 · 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.

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

Citations1
Published2022
Admission routes2
Has abstractyes

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