Line-of-Sight Path Following Control on UAV with Sideslip Estimation and Compensation
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a new online reconfigurable line-of-sight (LOS) path following control approach for an unmanned aerial vehicle (UAV). First, a time-varying lookahead distance mechanism is developed for guaranteeing agile and abrupt actions of the UAV by moving it towards the desired path from which the UAV is far away, while generating more smooth operations of the UAV to reduce the fluctuations when it is close to the demanded path. Then, a self-adjustable integral LOS guidance strategy is devised to effectively compensate the steady-state errors and sideslip angles which are caused by the negative impacts from wind. The neural network technique is employed for learning and regulating control parameters of the proposed guidance law online in order to precisely counteract the adverse effects of time-varying wind-induced sideslips. Finally, extensive simulation studies are carried out to demonstrate the effectiveness of the proposed path following methodology.
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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) | 0.000 | 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) | 0.000 | 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