Distributed Optimal Resource Allocation for High-Order Nonlinear Multiagent Systems Over Unbalanced Digraphs
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Bibliographic record
Abstract
In this article, we consider the distributed optimal resource allocation problem with multiple coupled equality constraints for strict-feedback multiagent systems (MASs) over unbalanced digraphs. To solve this problem, a novel integrated distributed control strategy consisting of a set of optimal reference generators and a group of tracking controllers is proposed. The reference generator is based on the estimation of the left eigenvector of the Laplacian matrix and is suitable for unbalanced digraphs. Moreover, the backstepping design technique is efficiently combined with the distributed optimization scheme, leading to a systematic solution for the high-order nonlinear MAS. It is proven that all the outputs of the MAS exponentially converge to the optimal solution of the resource allocation problem under the proposed control. Compared with the existing optimal resource allocation strategies for MASs, the proposed control strategy is applicable to high-order nonlinear MASs and shows favorable exponential convergence, even for unbalanced digraphs. Finally, the simulation results illustrate the above-mentioned theoretical findings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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