Distributed coordination for a class of non‐linear multi‐agent systems with regulation constraints
Why this work is in the frame
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Bibliographic record
Abstract
In this study, a multi‐agent coordination problem with steady‐state regulation constraints is investigated for a class of non‐linear systems. Unlike the existing leader‐following coordination formulations, a reference signal is not given by a dynamic autonomous leader but determined as the optimal solution of a distributed optimisation problem. Furthermore, the authors consider a global constraint having noisy data observations for the optimisation problem, which implies that the reference signal is not trivially available with the existing optimisation algorithms. To handle these challenges, the authors present a passivity‐based analysis and design approach by using only local objective function, local data observation and exchanged information from their neighbours. The proposed distributed algorithms are shown to achieve the optimal steady‐state regulation by rejecting the unknown observation disturbances for passive non‐linear agents, which are persuasive in various practical problems. Applications and simulation examples are then given to verify the effectiveness of the proposed design.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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