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
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Shrnutí: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 uncharacterized system nonlinearities are approximated by a fuzzy function embedded in an actor-critic reinforcement learning paradigm. To address input constraints and alleviate communication burden, an event-triggered scheme is introduced to update control signals efficiently. Additionally, a secure data-exchange mechanism in light of Paillier cryptographic scheme is designed to safeguard the privacy of state information during transmission. Two comprehensive simulations are performed to validate the feasibility and performance of the developed strategy.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2025.108238