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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Bibliographic Details
Published in:IEEE transactions on fuzzy systems Vol. 32; no. 3; pp. 804 - 815
Main Authors: Cai, Xiao, Shi, Kaibo, Sun, Yanbin, Cao, Jinde, Wen, Shiping, Tian, Zhihong
Format: Journal Article
Language:English
Published: New York IEEE 01.03.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Summary: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-batch descent gradient algorithm to optimize trigger thresholds, thereby reducing bandwidth usage. In addition, we introduce asymmetric Lyapunov-Krasovskii functions to decrease the number of decision variables, which improves the reliability and robustness of the control algorithm. Finally, we propose an intelligent event-triggered controller supervised by mini-batch machine learning and validate it on the joint CarSim-Simulink platform. Experimental results demonstrate that our approach reduces the sensitivity of autonomous vehicle systems to network fluctuations while ensuring system stability under network congestion caused by DoS attacks.
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2023.3308933