Leader–Follower Consensus Control of Multiple Quadcopters Under Communication Delays
Why this work is in the frame
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Bibliographic record
Abstract
This paper develops a novel decentralized leader–follower consensus algorithm for multiple-quadcopter systems under uniform constant and asynchronous time-varying communication delays. The consensus problem is formulated as the stability analysis and static controller design problem of a delayed system by defining the consensus error dynamics. Lyapunov-based methods along with the linear matrix inequality (LMI) techniques are utilized to derive the sufficient conditions for the control gain design that ensure asymptotic consensusability in the constant delay case, and consensus with bounded errors in the time-varying delay case. Also the computational complexity of solving control gains can be significantly reduced by decomposing the sufficient conditions into a set of equivalent low-dimensional conditions under undirected communication topologies. Simulation results show that larger systems are generally more susceptible to communication delays, and systems are more robust to delays when more followers are directly connected to the leader.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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