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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Vydané v:	Neural networks Ročník 195; s. 108238
Hlavní autori:	Huang, Jiaxin, Liu, Xiaoyang, Shen, Sikai, Yu, Wenwu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Ltd 01.03.2026
Predmet:	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
On-line prístup:	Získať plný text
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