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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Bibliographic Details
Published in:IEEE internet of things journal Vol. 12; no. 12; pp. 22036 - 22047
Main Authors: Lin, Na, Peng, Huiming, Chi, Ronghu
Format: Journal Article
Language:English
Published: Piscataway IEEE 15.06.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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 equivalent linearization data model, where additional parameters are used to reflect the uncertainties of the MAS. Assume that the communication among agents is subject to a stochastic FDI attack which is modeled by a weighted sum of attacks for adjacent communication channels. Then, combining the event-triggering condition along the iterative direction, an event-triggered data-driven iterative learning control (ET-DDILC) is proposed where the attacked information is used in control law and parameter estimation law to counteract the impact of FDI attacks. The convergence is proven by introducing additional tools of mathematical expectations and matrix theory. Moreover, the proposed ET-DDILC is further extended to the MASs under iteration-switching topologies. Extensive simulation results verify that the proposed ET-DDILC can achieve a good control performance against injection attacks without using any model information while simultaneously saving system resources through the event-triggering mechanism.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2025.3549604