Event-based non-fragile synchronization control for T-S fuzzy neural systems with cyber-attacks
Event-based non-fragile synchronization control for T-S fuzzy neural systems with cyber-attacks
- Author(s): Y. Yuan 1 ; Y. Liu 1 ; Y. Tan 2
- DOI: 10.1049/icp.2021.1419
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- Author(s): Y. Yuan 1 ; Y. Liu 1 ; Y. Tan 2
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View affiliations
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Affiliations:
1:
School of Applied Mathematics, Nanjing University of Finance and Economics , Nanjing, Jiangsu 210023 , P. R. China ;
2: School of Applied Mathematics, Nanjing University of Finance and Economics, Nanjing , Jiangsu 210023 , P. R. China
Source:
Jiangsu Annual Conference on Automation (JACA 2020),
2021
p.
51 – 56
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Affiliations:
1:
School of Applied Mathematics, Nanjing University of Finance and Economics , Nanjing, Jiangsu 210023 , P. R. China ;
- Conference: Jiangsu Annual Conference on Automation (JACA 2020)
- DOI: 10.1049/icp.2021.1419
- ISBN: 978-1-83953-563-5
- Location: Zhenjiang, Jiangsu Province, China
- Conference date: 13-15 November 2020
- Format: PDF
This paper is concerned with the adaptive event-triggered non-fragile synchronization secure control issue of T-S fuzzy neural networked system. To reduce unnecessary signal transmission as much as possible under the premise of ensuring performance, an adaptive event-triggered scheme is proposed, which can dynamically adjust the triggering threshold according to the output error. Based on the consideration of the impact of adaptive event-triggered scheme and network attacks on signal transmission, a novel synchronization error system model is constructed. On the basis of the system model, some sufficient conditions are obtained such that the error system is asymptotically stable by employing Lyapunov functional method. Moreover, by using LMIs method, the design problem of non-fragile T-S fuzzy controller is solved. Finally, a numerical simulation is provided to verify the validity of the designed approach.
Inspec keywords: Lyapunov methods; synchronisation; delays; control system synthesis; asymptotic stability; fuzzy neural nets; linear matrix inequalities; fuzzy control; stochastic systems; neurocontrollers
Subjects: Fuzzy control; Control system analysis and synthesis methods; Algebra; Stability in control theory