Dynamic Event-Triggered Consensus Control of Multi-Agent Systems With Time-Varying Delays and Semi-Markovian Switching Topology

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. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i>—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.