Dynamic Event-Triggering Mechanism and Its Application to Dynamic Average Tracking

• 成果类型: Article
• 署名作者: Xu, Tao; Yi, Xiaojian; Duan, Zhisheng; Chen, Guanrong; Wen, Guanghui
• 署名单位: Beijing Institute of Technology; Nanyang Technological University; Beijing Institute of Technology; Peking University; City University of Hong Kong; Southeast University - China
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3524432
• 发表日期: 2025
• 页码: 4548-4561
• 关键词: aircraft; estimation; Symmetric matrices; Multi-agent systems; Heuristic algorithms; Protocols; Linear systems; Laplace equations; Global Positioning System; vectors; Adaptive control; dynamic average tracking (DAT); dynamic event-triggering mechanism; initialization; linear multiagent system

摘要

As reported in the studies of dynamic average tracking, many existing solutions are not robust to initialization, in the sense that their design and implementation require specific and stringent initial conditions. All agents need to be reinitialized if the original initial conditions are violated due to network disruptions. This article aims to overcome this common issue existing in the control problem of distributed event-driven dynamic average tracking for networked multiple linear systems with linear reference signals. Utilizing only local information of each agent and its neighbors, an adaptive distributed event-driven estimation algorithm is designed to estimate the average reference signal, and an adaptive distributed event-driven control protocol is developed to regulate the system state. The main contributions of this work are two-fold. First, a couple of dynamic distributed event-triggering mechanisms are proposed. They enable the communication between neighboring agents to be performed intermittently and asynchronously, without sacrificing any convergence precision of the dynamic average tracking error. Second, the event-driven estimation algorithm and control protocol developed for general linear reference signals and multiple agents exhibit robustness to initialization and adaptability in parameter selection, since their operation does not depend on any specific initial conditions and global information. Finally, numerical simulations are presented to demonstrate the effectiveness of the theoretical results.