ℋ∞\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\cal H}_\infty}$$\end{document} Synchronization of Fuzzy Neural Networks Based on a Dynamic Event-triggered Sliding Mode Control Method

This paper focuses on the ℋ∞\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\cal H}_\infty}$$\end{document} 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 ℋ∞\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\cal H}_\infty}$$\end{document} 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.