On the Effect of Flexibility on the Dynamics of a Suspended Payload Carried by a Quadrotor
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) have been gaining increased importance due to their variety of applications. In some specific tasks, UAVs require the addition of payloads and onboard components, including sensors, which require great stability to provide safe and reliable responses (related to the payload characteristics, such as the temperature, pressure, vibrations and many other factors). In contrast with the suspended payloads carried by a quadrotor aircraft with a rigid attachment, an elastic attachment is designed to assess the influence of the vibration characteristics on the quadrotor and its payload. Since the payload dynamics can alter the flight performance, sensor measurement accuracy and payload integrity, an adapted sliding mode control is used to guide the quadrotor on its desired trajectory and to compensate for the payload dynamics. To reduce the need for position sensors, a reduced-dimension observer is designed to estimate the payload trajectory, as well as the external disturbance behavior. Numerical simulations are performed to demonstrate that the flexibility influences the quadrotor’s dynamics and can create residual oscillation on its payload.
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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 | 0.001 |
| 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) | 0.001 | 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".