Path-Following Control of A Quadrotor UAV With A Cable-Suspended Payload Under Wind Disturbances
Bibliographic record
Abstract
A path-following controller based on an uncertainty and disturbance estimator (UDE) for a quadrotor with a cable-suspended payload is proposed in this paper. The quadrotor and the payload are subject to unknown wind disturbances. The controller resembles a cascade architecture. For the outer loop, a UDE-based translational control law is proposed. The controller asymptotically stabilizes the quadrotor along a given path and estimates the lumped disturbances with a low-pass filter. For the inner loop, an attitude tracking controller is used to control the direction of the lift vector so that the actual lift force can asymptotically follow the reference force generated by the translational controller. The stability of the system with the translational controller and the attitude tracking controller has been shown to be asymptotically stable using the reduction theorem. With the help of the reduction theorem, the design of the translational and the attitude control can be decoupled, providing the flexibility of implementing different attitude controllers without redoing the stability analysis. As shown in the simulation, the control law can stabilize the quadrotor on the desired path under different wind disturbances.
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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) | 0.001 | 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".