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Record W4406608025 · doi:10.1139/dsa-2024-0031

Design and experimentation of a payload control system for vertical lifting operations via a tethered single-airplane

2025· article· en· W4406608025 on OpenAlexaffvenue
Jessy Verrette, David Rancourt

Bibliographic record

VenueDrone Systems and Applications · 2025
Typearticle
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAirplanePayload (computing)Control (management)AeronauticsEngineeringComputer scienceAutomotive engineeringAerospace engineeringComputer securityArtificial intelligence

Abstract

fetched live from OpenAlex

Some unconventional vertical lifting techniques making use of tethered airplanes combine the high efficiency of airplanes with the vertical lifting ability of helicopters. The main challenge consists of accurately controlling the position of the centralized payload. Kilometre-long tether configurations, subject to aerodynamic damping, were studied to reduce the orbit radius of the payload, but they are sensitive to wind and flight path deviations. This article presents the mechanical architecture, numerical model, and control strategy of a payload control system (PCS) for a circling single-airplane tethered lifting system. The PCS is mounted onto the payload, and it compensates for external perturbations and the horizontal force acting on the payload from the tether due to the single-airplane configuration. Experimental flights were conducted using a DJI Matrice 600 drone equipped with a 31 m long tether to mimic the trajectory of a circling airplane. During these flights, the PCS maintained payloads ranging from 1.6 to 4.8 kg at ∼10 cm from the target position. The PCS is a key component of this novel vertical lifting method, which can provide an alternative to helicopters and multirotor drones for cargo delivery or aerial work operations.

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 score0.980
Threshold uncertainty score0.457

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 head0.009
GPT teacher head0.225
Teacher spread0.216 · 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
Published2025
Admission routes2
Has abstractyes

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