An LMI approach to optimal consensus seeking in multi-agent systems
Bibliographic record
Abstract
In this paper an optimal control design strategy to guarantee consensus achievement in a multi-agent network 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 the consensus achievement. Therefore, the linear matrix inequality (LMI) formulation is used to solve the corresponding optimization problem and simultaneously to address the consensus achievement constraint. Moreover, using the LMI formulation a controller specific structure based on the neighboring sets can be imposed as an additional LMI constraint. Therefore, the only information each controller needs is the one it receives from its associated neighbors in its neighboring set. The global cost function formulation provides more insight into the optimal performance of the entire network and would result in a ldquoglobalrdquo optimal (or suboptimal) solution. Simulation results are presented to illustrate the performance of the multi-agent team in achieving consensus.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".