Dynamic Event-Triggering-based Average Tracking Control for Multi-Agent Systems

This paper addresses the problem of dynamic event-triggering tracking control for multi-agent systems (MASs). MASs have gained significant attention due to their abilities in collaborative problem-solving in various domains such as robotics, distributed sensing, and autonomous networks. Despite advancements in distributed algorithms and consensus protocols, challenges such as communication latency, energy constraints, and robustness against dynamic uncertainties persist. To overcome these, the paper proposes an adaptive framework leveraging dynamic event-triggered control. This approach reduces communication overhead and computational resource utilization by updating control inputs only when specific events occur, based on the system’s state deviations beyond predefined thresholds. This paper focuses on average tracking control, where each agent aims to track a common reference signal. By integrating event-triggered mechanisms with adaptive triggering thresholds and hybrid paradigms, this paper presents a robust adaptive distributed event-triggered dynamic average tracking control scheme. The proposed scheme ensures exponential convergence of tracking errors without requiring specific initialization conditions or global information. Finally, a numerical example is conducted to verify the proposed tracking control approach.