Design and experimentation of a payload control system for vertical lifting operations via a tethered single-airplane
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".