Data-driven leader-following consensus for nonlinear multi-agent systems against composite attacks: A twins layer approach

This paper studies the leader-following consensuses of uncertain and nonlinear multi-agent systems against composite attacks (CAs), including denial of service (DoS) attacks and actuation attacks (AAs). A double-layer control framework is formulated, where a digital twin layer (TL) is added beside t...

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Bibliographic Details
Published in:Journal of the Franklin Institute Vol. 361; no. 14; p. 107067
Main Authors: Gong, Xin, Zhang, Zhipeng, Shen, Jun
Format: Journal Article
Language:English
Published: Elsevier Inc 01.09.2024
Subjects:
Cyber attacks
Data-driven
Leader-following consensus
Model-free adaptive control
Nonlinear multi-agent systems
Twin layer
Cyber attacks
Nonlinear multi-agent systems
Data-driven
Model-free adaptive control
Twin layer
Leader-following consensus
ISSN:0016-0032
Online Access:Get full text
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Summary:This paper studies the leader-following consensuses of uncertain and nonlinear multi-agent systems against composite attacks (CAs), including denial of service (DoS) attacks and actuation attacks (AAs). A double-layer control framework is formulated, where a digital twin layer (TL) is added beside the traditional cyber–physical layer (CPL), inspired by the recent Digital Twin technology. Consequently, the resilient control task against CAs can be divided into two parts: One is distributed estimation against DoS attacks on the TL, and the other is resilient decentralized tracking control against actuation attacks on the CPL. First, a distributed observer based on switching estimation law against DoS is designed on TL. Second, a distributed model-free adaptive control (DMFAC) protocol based on attack compensation against AAs is designed on CPL. Moreover, the uniformly ultimately bounded convergence of consensus error of the proposed double-layer DMFAC algorithm is strictly proved. Finally, the simulation verifies the effectiveness of the resilient double-layer control scheme.
ISSN:0016-0032
DOI:10.1016/j.jfranklin.2024.107067