Event-Triggered Fully Distributed Control: A Model-Free Adaptive Learning Algorithm

• 成果类型: Article
• 署名作者: Ma, Yong-Sheng; Che, Wei-Wei; Wu, Zheng-Guang
• 署名单位: Northeastern University - China; Zhejiang University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3548937
• 发表日期: 2025
• 页码: 5576-5583
• 关键词: Event detection; TOPOLOGY; Adaptation models; data models; Design methodology; Network topology; filters; Eigenvalues and eigenfunctions; adaptive learning; training; Event-triggered scheme; fully distributed control; model-free adaptive learning; multiagent systems (MASs)

摘要

This article studies the consensus problem in multiagent systems under the challenge of an unknown system model and limited communication resources. A novel model-free adaptive learning algorithm is developed to learn the controller from system data. A model-based event-triggered fully distributed control (ET-FDC) algorithm is proposed to achieve consensus while saving the limited communication resources. Furthermore, a data-driven systematic learning methodology for the ET-FDC algorithm is introduced to eliminate the need for a system model. Compared with existing approaches, the main advantages of the proposed method are that it not only avoids using the system model and global topology information, but also reduces unnecessary communication. Finally, simulations are presented to illustrate the superiority of the theoretical results.