Event-Triggered Data-Driven Iterative Learning Control for Multiagent Systems With FDI Attacks

This work investigates the consensus learning control for heterogeneous nonlinear multiagent systems (MASs) under false data injection (FDI) attacks on the communication channels. An enhanced iterative dynamic linearization (EiDL) method is introduced to transform the nonlinear MAS into an equivalen...

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Vydáno v:	IEEE internet of things journal Ročník 12; číslo 12; s. 22036 - 22047
Hlavní autoři:	Lin, Na, Peng, Huiming, Chi, Ronghu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 15.06.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Adaptation models Channels Communication channels Control systems Control theory Convergence Data models Data-driven iterative learning control (ILC) Denial-of-service attack Event detection event-triggered control (ETC) false data injection (FDI) attacks Internet of Things Iterative methods Learning Linearization Matrix theory Multiagent systems multiagent systems (MASs) Nonlinear control Nonlinear systems Parameter estimation Switches Topology Vectors
ISSN:	2327-4662, 2327-4662
On-line přístup:	Získat plný text
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