Robust Control of Four-Rotor Unmanned Aerial Vehicle With Disturbance Uncertainty
Why this work is in the frame
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Bibliographic record
Abstract
This paper addresses the stability and tracking control problem of a quadrotor unmanned flying robot vehicle in the presence of modeling error and disturbance uncertainty. The input algorithms are designed for autonomous flight control with the help of an energy function. Adaptation laws are designed to learn and compensate the modeling error and external disturbance uncertainties. Lyapunov theorem shows that the proposed algorithms can guarantee asymptotic stability and tracking of the linear and angular motion of a quadrotor vehicle. Compared with the existing results, the proposed adaptive algorithm does not require an a priori known bound of the modeling errors and disturbance uncertainty. To illustrate the theoretical argument, experimental results on a commercial quadrotor vehicle are presented.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it