Reinforcement learning-based funnel control and privacy preservation for multi-agent systems with input dead-zone

This paper investigates the privacy-preserving protocol and reinforcement learning-based funnel controller design of multi-agent systems subject to input dead-zone constraints. An adaptive funnel controller is formulated to guarantee that the tracking errors keep within prescribed boundaries. The un...

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Veröffentlicht in:	Neural networks Jg. 195; S. 108238
Hauptverfasser:	Huang, Jiaxin, Liu, Xiaoyang, Shen, Sikai, Yu, Wenwu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.03.2026
Schlagworte:	Event-triggered strategy Funnel control Input dead-zone Nonlinear multi-agent systems Privacy preservation Reinforcement learning Input dead-zone Nonlinear multi-agent systems Privacy preservation Funnel control Event-triggered strategy Reinforcement learning
ISSN:	0893-6080, 1879-2782, 1879-2782
Online-Zugang:	Volltext
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