Adaptive Event-Triggered Output Synchronization of Heterogeneous Multiagent Systems: A Model-Free Reinforcement Learning Approach

This paper proposes a reinforcement learning approach to the output synchronization problem for heterogeneous leader-follower multi-agent systems, where the system dynamics of all agents are completely unknown. First, to solve the challenge caused by unknown dynamics of the leader, we develop an exp...

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Veröffentlicht in:	IEEE transactions on signal and information processing over networks Jg. 11; S. 604 - 616
Hauptverfasser:	Hu, Wenfeng, Wang, Xuan, Guo, Meichen, Luo, Biao, Huang, Tingwen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Algorithms Dynamic programming Event detection Event-triggered mechanism (ETM) experience-replay heterogeneous multi-agent systems (HMASs) Heuristic algorithms Machine learning Multi-agent systems Multiagent systems Observers Optimal control output synchronization Power system dynamics Reinforcement learning reinforcement learning (RL) Synchronism Synchronization System dynamics Teaching methods Vehicle dynamics
ISSN:	2373-776X, 2373-7778
Online-Zugang:	Volltext
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