Optimal Consensus Seeking in a Network of Multiagent Systems: An LMI Approach
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, an optimal control design strategy for guaranteeing consensus achievement in a network of multiagent systems is developed. Minimization of a global cost function for the entire network guarantees a stable consensus with an optimal control effort. In solving the optimization problem, it is shown that the solution of the Riccati equation cannot guarantee consensus achievement. Therefore, a linearmatrix-inequality (LMI) formulation of the problem is used to address the optimization problem and to simultaneously resolve the consensus achievement constraint. Moreover, by invoking an LMI formulation, a semidecentralized controller structure that is based on the neighboring sets, i.e., the network underlying graph, can be imposed as an additional constraint. Consequently, the only information that each controller requires is the one that it receives from agents in its neighboring set. The global cost function formulation provides a deeper understanding and insight into the optimal system performance that would result from the global solution of the entire network of multiagent systems. Simulation results are presented to illustrate the capabilities and characteristics of our proposed multiagent team in achieving consensus.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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