Development of a Payload Control System for a Single-airplane Tethered Lifting System
Bibliographic record
Abstract
Vertical lifting methods using circling airplanes tethered to a centralized payload have been studied since the 1940s. These methods combine the high efficiency of fixed-wing airplanes with the vertical lifting ability of helicopters. However, such lifting systems must tackle the challenge of accurately controlling the position of the centralized payload in order to be viable. Typically, a kilometer-long tether configuration, subject to aerodynamic damping, is studied to achieve a small orbit radius for the payload, resulting in nearly stationary movement. This article presents the development of a payload control system (PCS) for a circling single-airplane tethered lifting system. A PCS mounted onto the payload compensates for flight path deviations of the airplane and allows the use of a shorter tether because it removes the dependency on aerodynamic forces to position the payload. This article presents the mechanical architecture and the control strategy of the PCS, along with experimental flights done under a DJI Matrice 600 drone to mimic the trajectory of a circling single-airplane. The DJI drone followed a circular path of 16 m in diameter with a period of 14 s during which a payload, including the PCS, was linked to the drone with a 31 m (102 ft) tether. During the experiments, the PCS maintained payloads ranging from 1.6 kg (3.5 lb) to 4.8 kg (10.6 lb) at ∼10 cm (∼4 in) of the target position, regardless of the trajectory deviations of the DJI drone. The PCS is a key feature of this novel vertical lifting method which has the potential to provide an alternative to rotorcraft and multi-rotor drones for cargo delivery
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".