Leader-following consensus control of multi-agent systems with communication delays & random packet loss
Why this work is in the frame
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Bibliographic record
Abstract
The objective of this paper is to develop a novel consensus control algorithm for multi-agent systems (MAS) experiencing constant and time-varying communication delays along with random packet data loss. The topology of the communication network of the system is modeled by an algebraic graph theory. The multi-agent dynamics is based on double integrator systems in discrete time domain. Bernoulli distribution principle is applied to system dynamics in order to deal with the uncertain packet data loss. The sufficient conditions for the stabilization of controller are formed by using Lyapunov-based methodologies and linear matrix inequalities (LMIs) techniques. The feasibility of the generated LMIs are analyzed to verify the stability of controller design. The control gain of the system is calculated if the solution of LMIs lie within the feasibility region, which ensures the MAS to achieve consensus. Simulation results with three agents are shown to verify the consensus of the proposed system.
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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 | 0.000 | 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