Cooperative Output Regulation With Asynchronous Transmissions and Time-Varying Delays
Bibliographic record
Abstract
This article revisits the sampled-data cooperative output regulation problem in a hybrid system framework. Due to the inevitable network-induced imperfections caused by communication among agents, we consider the problem under asynchronous transmissions and time-varying delays. A hybrid system model is introduced to incorporate the aforementioned communication imperfections and facilitate the analysis on internal stability and$\mathcal {L}_2$($\mathcal {L}_\infty$) performance of closed-loop systems. A novel Lyapunov functional candidate is proposed to establish the stability condition in terms of maximally allowable transmission intervals (MATIs) and maximally allowable delays (MADs). Compared with some existing results, the proposed Lyapunov functional candidate provides clearer physical significance. As a result, more straightforward computations and tradeoff designs on MATIs and MADs can be provided. Finally, numerical simulations are given to illustrate the efficiency and feasibility of the obtained results.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".