Dynamic Event-Triggered Consensus Control of Multi-Agent Systems With Time-Varying Delays and Semi-Markovian Switching Topology
Bibliographic record
Abstract
In this paper, we address the consensus problem in multi-agent systems (MASs) under conditions where time-varying delays impact inter-agent transmissions and the communication topology changes according to semi-Markovian rules with partially unknown transition rates. We implement dynamic event-triggering mechanisms (DETMs) on both the sensor-to-observer (S-O) and controller-to-actuator (C-A) channels to minimize unnecessary data transmissions within the network, which involves utilizing locally triggered sampled data in a distributed manner to optimize resource efficiency. In this output-feedback design, each agent constructs distributed observers to predict its own and neighboring agents’ states. In the design phase, we convert the consensus control problem into an asymptotic stability problem. Employing the Lyapunov-Krasovskii approach, we formulate the event-triggering parameters to ensure the stability of the closed-loop system comprising all agents, thereby achieving consensus. Through numerical simulations and experiments, we demonstrate that our approach effectively balances reducing the frequency of inter-agent communication with ensuring that the agents reach consensus.Note to Practitioners—In many industrial applications of multi-agent systems (MASs), such as robotics and sensor networks, achieving coordinated control in the presence of time-varying communication delays and changing topologies is a significant challenge. This paper presents a solution using dynamic event-triggered mechanisms (DETMs) to reduce unnecessary data transmissions between agents, ensuring efficient resource utilization. Unlike traditional methods that rely on constant data flow, DETMs allow communication only when necessary, optimizing bandwidth usage without sacrificing performance. By employing a distributed observer-based approach, each agent estimates its own state and its neighbors’ states, overcoming time-varying delays. The method offers a promising solution for improving efficiency in MASs. Future research could extend this approach to handle larger networks, more complex topologies, and varying operational conditions, with potential applications in autonomous vehicles, drone fleets, and large-scale sensor networks.
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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 | 0.001 |
| 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".