Intelligent Event-Triggered Control Supervised by Mini-Batch Machine Learning and Data Compression Mechanism for T-S Fuzzy NCSs Under DoS Attacks

This article presents a comprehensive solution to mitigate network congestion in T-S fuzzy networked control systems caused by denial-of-service (DoS) attacks and quality-of-service (QoS) queuing mechanisms. We develop a novel data compression mechanism to alleviate network congestion and use a mini...

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Veröffentlicht in:	IEEE transactions on fuzzy systems Jg. 32; H. 3; S. 804 - 815
Hauptverfasser:	Cai, Xiao, Shi, Kaibo, Sun, Yanbin, Cao, Jinde, Wen, Shiping, Tian, Zhihong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.03.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Asymptotic stability Congestion Control theory Cyber security Data compression Delays Denial of service attacks Event triggered control Fuzzy control Fuzzy systems Lyapunov–Krasovskii function (LKF) Machine learning Mathematical analysis Network control Protocols Queueing Reliability Robust control Security Stability criteria Systems stability T-S fuzzy networked control system
ISSN:	1063-6706, 1941-0034
Online-Zugang:	Volltext
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