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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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2025.108238