Optimal Distributed Control for Leader-Follower Networks: A Scalable\n Design
Bibliographic record
Abstract
The focus of this paper is directed towards optimal control of multi-agent\nsystems consisting of one leader and a number of followers in the presence of\nnoise. The dynamics of every agent is assumed to be linear, and the performance\nindex is a quadratic function of the states and actions of the leader and\nfollowers. The leader and followers are coupled in both dynamics and cost. The\nstate of the leader and the average of the states of all followers (called\nmean-field) are common information and known to all agents; however, the local\nstate of the followers are private information and unknown to other agents. It\nis shown that the optimal distributed control strategy is linear time-varying,\nand its computational complexity is independent of the number of followers.\nThis strategy can be computed in a distributed manner, where the leader needs\nto solve one Riccati equation to determine its optimal strategy while each\nfollower needs to solve two Riccati equations to obtain its optimal strategy.\n This result is subsequently extended to the case of the infinite horizon\ndiscounted and undiscounted cost functions, where the optimal distributed\nstrategy is shown to be stationary. A numerical example with $100$ followers is\nprovided to demonstrate the efficacy of the results.\n
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".