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\({{\cal H}_\infty}\) Synchronization of Fuzzy Neural Networks Based on a Dynamic Event-triggered Sliding Mode Control Method

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  • Control Theory and Applications
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Abstract

This paper focuses on the \({{\cal H}_\infty}\) synchronization issue for fuzzy neural networks via a dynamic event-triggered sliding mode control scheme. In order to relieve the congestion phenomenon in the communication channel, a dynamic event-triggered mechanism is introduced into the sliding mode control design, in which an internal dynamical variable is adopted to fit the event-triggered condition suitably. Moreover, some results with less conservatism are obtained by considering the asynchronous premise variable problem. Then, sufficient criteria are established through the Lyapunov stability theory, which can guarantee that the sliding mode dynamics is asymptotically stable with a given \({{\cal H}_\infty}\) performance. In this case, a dynamic event-triggered sliding mode control law is constructed to drive the trajectories of the fuzzy neural networks onto the designed sliding surface. Finally, the effectiveness and superiority of the presented method is verified by an illustrative example.

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Author information

Authors and Affiliations

  1. AnHui Province Key Laboratory of Special Heavy Load Robot and the School of Electrical Engineering and Information, Anhui University of Technology, Anhui, 243032, China

    Hebao Jia, Jing Wang & Hao Shen

  2. School of Automation and Electrical Engineering, Linyi University, Linyi, 276005, China

    Xiangyong Chen

  3. School of Information Science and Engineering, Chengdu University, Chengdu, 610106, China

    Kaibo Shi

Authors
  1. Hebao Jia
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  2. Jing Wang
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  4. Kaibo Shi
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  5. Hao Shen
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Corresponding authors

Correspondence to Jing Wang or Xiangyong Chen.

Additional information

Hebao Jia is now an M.S. candidate at the School of Electrical and Information Engineering, Anhui University of Technology, China. His current research interests include sliding mode control, neural network, and event-triggered mechanism.

Jing Wang received her Ph.D. degree in electric power system and automation from Hohai University in 2019. Since 2011, she has been with Anhui University of Technology, China, where she is currently an Associate Professor. Her current research interests include nonlinear control, complex networks, and power systems.

Xiangyong Chen received his Ph.D. degree in control theory and control engineering from the Northeastern University, China, in 2008 and 2012, respectively. He is an Associate Professor of College of Automation and Electrical Engineering at the Linyi University. From July 2014 to April, 2019, he was a Post-doctoral student in the Department of Mathematics, Southeast University, China. From April 1, 2016 to December 31, 2017, he was a Visiting Scholar in the Department of Electrical Engineering at the Yeungnam University, Korea. His current research interests include complex dynamic system and complex networks, and synchronization control of chaotic systems.

Kaibo Shi received his Ph.D. degree in the School of Automation Engineering at the University of Electronic Science and Technology of China. He is a professor of School of Information Sciences and Engineering, Chengdu University. From September 2014 to September 2015, he was a visiting scholar at the Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada. His current research interests include stability theorem, robust control, sampled-data control systems, and neural networks. He is the author or coauthor of over 60 research articles. He is a very active reviewer for many international journals.

Hao Shen received his Ph.D. degree in control theory and control engineering from Nanjing University of Science and Technology, Nanjing, China, in 2011. From 2013 to 2014, he was a Post-Doctoral Fellow with the Department of Electrical Engineering, Yeungnam University, Korea. Since 2011, he has been with Anhui University of Technology, China, where he is currently a Professor and a Doctoral Supervisor. His current research interests include stochastic hybrid systems, complex networks, fuzzy systems and control, and nonlinear control. Prof. Shen was a recipient of the Highly Cited Researcher Award by Clarivate Analytics (formerly, Thomson Reuters) in 2019 and 2020.

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Jia, H., Wang, J., Chen, X. et al. \({{\cal H}_\infty}\) Synchronization of Fuzzy Neural Networks Based on a Dynamic Event-triggered Sliding Mode Control Method. Int. J. Control Autom. Syst. 20, 1882–1890 (2022). https://doi.org/10.1007/s12555-021-0470-9

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