Asynchronous robust dynamic output feedback H∞ control for fuzzy stochastic hybrid systems subject to time-varying delays and hidden Markov model
In this paper, we studied the event-triggered dynamic output feedback asynchronous robust H ∞ control for Takagi–Sugeno fuzzy Markovian jump neural networks with mode-dependent time-varying delays. The main aim is to design fuzzy dynamic output feedback controller under asynchrony and event-triggere...
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| Published in | Soft computing (Berlin, Germany) Vol. 27; no. 1; pp. 201 - 218 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.01.2023
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| Abstract | In this paper, we studied the event-triggered dynamic output feedback asynchronous robust
H
∞
control for Takagi–Sugeno fuzzy Markovian jump neural networks with mode-dependent time-varying delays. The main aim is to design fuzzy dynamic output feedback controller under asynchrony and event-triggered mechanisms to ensure the stochastic stability and
H
∞
performance of closed-loop fuzzy Markovian jump neural networks with time-varying delays. Hidden Markov model strategy is used to describe asynchronous scheme; an event-triggered mechanism is utilized to reduce the consumption of communication resources. An improved mode-dependent and delay dependent L–K functional is constructed to address robust stochastic stability, and parallel distribution compensation technology is exploited to realize the fuzzy dynamic output feedback controller in terms of linear matrix inequalities. Finally, numerical examples including a synthetic transcriptional regulatory oscillatory network are provided to demonstrate the method’s efficiency. |
|---|---|
| AbstractList | In this paper, we studied the event-triggered dynamic output feedback asynchronous robust
H
∞
control for Takagi–Sugeno fuzzy Markovian jump neural networks with mode-dependent time-varying delays. The main aim is to design fuzzy dynamic output feedback controller under asynchrony and event-triggered mechanisms to ensure the stochastic stability and
H
∞
performance of closed-loop fuzzy Markovian jump neural networks with time-varying delays. Hidden Markov model strategy is used to describe asynchronous scheme; an event-triggered mechanism is utilized to reduce the consumption of communication resources. An improved mode-dependent and delay dependent L–K functional is constructed to address robust stochastic stability, and parallel distribution compensation technology is exploited to realize the fuzzy dynamic output feedback controller in terms of linear matrix inequalities. Finally, numerical examples including a synthetic transcriptional regulatory oscillatory network are provided to demonstrate the method’s efficiency. |
| Author | Sun, Wei Zhuang, Guangming Lin, Yuqian Xia, Jianwei |
| Author_xml | – sequence: 1 givenname: Yuqian surname: Lin fullname: Lin, Yuqian organization: School of Mathematical Sciences, Liaocheng University – sequence: 2 givenname: Guangming orcidid: 0000-0002-6552-4636 surname: Zhuang fullname: Zhuang, Guangming email: zgmtsg@126.com organization: School of Mathematical Sciences, Liaocheng University – sequence: 3 givenname: Jianwei surname: Xia fullname: Xia, Jianwei organization: School of Mathematical Sciences, Liaocheng University – sequence: 4 givenname: Wei surname: Sun fullname: Sun, Wei organization: School of Mathematical Sciences, Liaocheng University |
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| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Keywords | Dynamic output feedback Hidden Markov model Asynchronous control Neural networks Takagi–Sugeno fuzzy systems |
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| Snippet | In this paper, we studied the event-triggered dynamic output feedback asynchronous robust
H
∞
control for Takagi–Sugeno fuzzy Markovian jump neural networks... |
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| SubjectTerms | Artificial Intelligence Computational Intelligence Control Engineering Fuzzy Systems and Their Mathematics Mathematical Logic and Foundations Mechatronics Robotics |
| Title | Asynchronous robust dynamic output feedback H∞ control for fuzzy stochastic hybrid systems subject to time-varying delays and hidden Markov model |
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