Bipartite Event-Triggered Output Tracking Consensus of Heterogeneous Linear Multi-Agent Systems Under Switching Directed Topologies
Bibliographic record
Abstract
This paper investigates the bipartite event-triggered output consensus problem in heterogeneous linear multi-agent systems (MASs) with a leader operating under signed jointly connected digraphs. The research addresses both cooperative and adversarial communication among agents by introducing a novel edge-based bipartite event-triggering mechanism (ETM), as well as a dynamic ETM for communication between the leader and followers. Subsequently, a distributed bipartite compensator utilizing the composite ETMs is proposed to estimate the states of the leader, and serves as a reference for the states of followers. Moreover, a significant feature of the compensator is that it reduces the frequency of communication between the leader and followers. Besides, it is proven that the system with the compensator can exclude Zeno behavior. Furthermore, observers designed to estimate the states of followers, as well as a new distributed control protocol, are proposed to address the output tracking problem of heterogeneous linear MASs. The results demonstrate that, through the proposed protocol, the output tracking error of the closed-loop control system converges to zero exponentially. Finally, the theoretical findings of this study are validated through a numerical example and an application example.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".