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Record W4380303899 · doi:10.1109/lcsys.2023.3285421

Control Framework for a UAV Slung-Payload Transportation System

2023· article· en· W4380303899 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Control Systems Letters · 2023
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPayload (computing)Control theory (sociology)Computer scienceTrajectorySwingCascadeStability (learning theory)Exponential stabilityControl engineeringControl systemStability theoryControl (management)EngineeringNetwork packetPhysics

Abstract

fetched live from OpenAlex

This paper presents a control framework for transporting a UAV slung-payload, which can asymptotically stabilize not only the UAV but also the tether swing angles. By separating the system into two subsystems, the cascade control methodology is used to design the framework, which includes two sufficient conditions and suits for a large class of existing controllers. The control framework is strictly proved with the boundness of all states such that it can guarantee the asymptotic stability of the closed-loop overall system over the entire configuration space. Then, this framework is applied to a UAV trajectory tracking control problem with stability analysis. Samples of controllers are presented in the type of saturation or dynamic feedback. Finally, numerical and experimental validations are carried out on a UAV slung-payload transportation.

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.

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)0.001
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: none
Teacher disagreement score0.937
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
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 head0.012
GPT teacher head0.221
Teacher spread0.209 · 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