Fully Distributed Edge-Based Dynamic Event-Triggered Control for Multiple Quadrotors
Bibliographic record
Abstract
This article investigates the fully distributed event-based time-varying formation control problem for multiple quadrotors with unknown perturbations, input saturation, and switching topologies. A novel adaptive dynamic event-triggered scheme is first formulated to alleviate the communication burden and reduce the resources consumption. Meanwhile, online triggering parameters and dynamic thresholds with updating laws associated with each edge are introduced so that the control protocol can be developed in a fully distributed way and the unnecessary communication can be further reduced for the leader–follower multiquadrotor system with switching topologies without sacrificing the tracking performance. Then, a fully distributed robust formation control protocol using the low gain feedback technique is developed to guarantee the time-varying formation of multiple quadrotors subject to unknown perturbations and input saturation without requiring global information. Furthermore, sufficient conditions are derived to ensure the asymptotic convergence of the formation error and Zeno-freeness. Hardware experiments are conducted to verify the efficiency of the designed controller.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".