Dynamic Event-Triggered Formation Control of Multi-Agent Systems With Non-Uniform Time-Varying Communication Delays

In this study, we address the challenge of time-varying formation control in multi-agent systems (MASs) in the presence of time-varying intra- and inter-agent communication delays. To tackle time-varying delays, we equip each agent with a bank of distributed observers to estimate its own and its neighbors’ states. We apply dynamic periodic event-triggered mechanisms to both sensor-to-observer (S-O) and controller-to-actuator (C-A) channels, aiming to reduce unnecessary data transmissions in the network by relying on locally triggered sampled data in a distributed fashion to enhance resource efficiency. In the design stage, we transform the state formation control problem into an asymptotic stability problem. Using the Lyapunov-Krasovskii functional (LKF) approach, we design the event-triggering parameters such that the closed-loop system of all agents is stable and agents reach the desired formation. Numerical simulations demonstrate that our approach achieves a balance by reducing inter-agent communication frequency while maintaining the desired formation. Finally, we illustrate the effectiveness and advantages of this approach through experiments on a real-world robotic system. Note to Practitioners—In practical applications of multi-agent systems, the use of a communication network introduces some challenging issues. To name a few, periodic sampling with a high frequency relies on heavy transmission of information between components, which may result in network congestion. Factors such as limited bandwidth, signal attenuation, and packet losses contribute to delays in networked MAS. Additionally, network security, protocols, buffering, processing, and transmission times play significant roles. Since network-induced delays depend heavily on variable network conditions, they are generally non-uniform and time-varying. This paper proposes a solution for formation control in MASs, considering communication delays, and holds practical implications across various industries. It can enhance coordination for tasks such as warehouse logistics and collaborative manufacturing in autonomous robotics. Drone swarms can benefit from more efficient and reliable movement coordination, impacting surveillance and precision agriculture. In industrial automation, synchronization among machines or robotic arms can be improved for increased efficiency. A noteworthy aspect of this paper is the validation of our results through experiments on a real-world multi-robot system, demonstrating broad applicability.