Distributed Adaptive Dynamic Event-Triggered Control for Multiple Quadrotors
Bibliographic record
Abstract
This article studies formation control problems for leader–follower multiquadrotor systems subject to unknown perturbations and limited resources via an event-triggered mechanism. A distributed adaptive dynamic event-triggered formation control protocol is designed by utilizing a sliding-mode control approach, such that the integral sliding-mode manifold can be reached in finite time for the states of the nonlinear, coupled, and underactuated system with unknown external disturbances. A distributed integral sliding-mode surface is proposed to guarantee the formation tracking performance as the state trajectories of multiquadrotor systems move on the constructed sliding manifold. Then, a novel adaptive dynamic triggering strategy is developed to adjust the triggering interval dynamically and, thus, reduce the unnecessary resource consumption. Via the Lyapunov stability theory and the Barbalat lemma, sufficient conditions to ensure the formation tracking results are derived for leader–follower multiquadrotor systems. Simulations and experiments to validate the effectiveness of the proposed control scheme are conducted.
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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 | 0.001 |
| 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 | 0.001 | 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".