Multi-Quadcopter Formation Control Using Sampled-Data Event-Triggered Communication With Gain Optimization
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Bibliographic record
Abstract
This paper addresses the distributed event-triggered leader-follower formation tracking control problem for multi- agent systems (MASs) with general linear agent dynamics in a sampled-data environment. A new distributed state-estimate- based event-triggering communication mechanism is proposed to manage the inter-agent communication at each sampling instant. Then, a formation control protocol is designed based on the combined measurement of all locally-available triggered sampled information for each follower agent. The event-generator and formation controller gains are co-designed from the sufficient linear matrix inequality (LMI) conditions that ensure the uniform ultimate boundedness for the closed-loop formation error dynamics. In addition, the free parameters in the derived conditions are optimized to produce event-generator and controller gains to maximize system performance. Lastly, the developed protocols were validated using simulations of a group of linearized miniature quadcopters.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 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