Distributed Observer-Based Formation-Fencing Control for Multi-Autonomous Aerial Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates the problem of distributed observer-based adaptive formation-fencing control for multi-unmanned aerial vehicles (UAVs) subject to parametric uncertainties, external disturbances and input saturation. For each following UAV, a novel fixed-time distributed observer is designed to estimate the time-varying state trajectory of the leader UAV within a directed topology. To effectively hunt down a target, we focus on transition from formation control to fencing control for a swarm of UAVs. A novel continuous switching function is proposed to accomplish this transition. Disturbance observers are leveraged to estimate external disturbances to tackle wind effects on the UAVs. Unmodeled dynamics are approximated by a fuzzy logic system. Then, by utilizing the Lyapunov stability theory, it is proven that the formation tracking errors converge to the small neighborhoods of the origin. Thereafter, utilizing the designed control input signals from the position subsystem, an adaptive fuzzy control method is proposed to bring stability to the attitude subsystem. Finally, simulation results are offered to demonstrate the theoretical results.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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