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

Cai, X; Shi, K; Sun, Y; Cao, J; Wen, S; Tian, Z

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

Cai, X Shi, K Sun, Y Cao, J Wen, S

Tian, Z

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Fuzzy Systems, 2023, PP, (99), pp. 1-11
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Cai, X
dc.contributor.author	Shi, K
dc.contributor.author	Sun, Y
dc.contributor.author	Cao, J
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.contributor.author	Tian, Z
dc.date.accessioned	2024-01-16T21:43:11Z
dc.date.available	2024-01-16T21:43:11Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Fuzzy Systems, 2023, PP, (99), pp. 1-11
dc.identifier.issn	1063-6706
dc.identifier.issn	1941-0034
dc.identifier.uri	http://hdl.handle.net/10453/174658
dc.description.abstract	This paper presents a comprehensive solution to mitigate network congestion in T-S fuzzy networked control systems (NCSs) 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. Additionally, we introduce asymmetric Lyapunov-Krasovskii functions (LKFs) 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 (IETC) 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 (AV) systems to network fluctuations while ensuring system stability under network congestion caused by DoS attacks.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Fuzzy Systems
dc.relation.isbasedon	10.1109/TFUZZ.2023.3308933
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4901 Applied mathematics
dc.title	Intelligent Event-Triggered Control Supervised by Mini-Batch Machine Learning and Data Compression Mechanism for T-S Fuzzy NCSs Under DoS Attacks
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2024-01-16T21:43:10Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

This paper presents a comprehensive solution to mitigate network congestion in T-S fuzzy networked control systems (NCSs) 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. Additionally, we introduce asymmetric Lyapunov-Krasovskii functions (LKFs) 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 (IETC) 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 (AV) systems to network fluctuations while ensuring system stability under network congestion caused by DoS attacks.

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http://hdl.handle.net/10453/174658