Observer based leader following consensus for multi-agent systems with random packet loss
Why this work is in the frame
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Bibliographic record
Abstract
This paper addresses the leader-follower consensus problem of multi-agent systems (MASs) consisting of general linear agents in the event of stochastic communication link failure over the network. Bernoulli process is applied to model the packet dropout during operation while the packet dropout in communication links are assumed to be asynchronous and independent. A distributed observer-type algorithm is proposed based on the sufficient conditions using Lyapunov-based method, linear matrix inequality (LMI) techniques and the separation principle. It is shown that the sufficient conditions can be decomposed into small conditions of same dimension as a single agent, provided that the followers are symmetrically connected, which leads to efficient solutions when considering consensus problem of a large group of high-order linear agents. Numerical simulations for groups of five double-integrator agents and three linearized quadcopter agents are conducted to demonstrate the effectiveness of the proposed algorithm.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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