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\begin{document}

\title{Event-triggered Adaptive Estimated Inverse Neural Network Control of
Uncertain Nonlinear Systems With Input Hysteresis Nonlinearity}



\author[1]{Jianyong Yao}%
\author[1]{Ning Zhou}%
\author[1]{Wenxiang Deng}%
\affil[1]{Nanjing University of Science and Technology School of Mechanical Engineering}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\begin{abstract}
This article investigates an event-triggered adaptive estimated inverse
control scheme for a class of uncertain nonlinear systems with
hysteresis effects, parametric uncertainties and disturbances. An online
estimated inverse hysteresis compensation mechanism is developed, where
an adaptive technique is employed to obtain the value of unknown
hysteresis parameters. Compared with the common approaches, its biggest
advantage lies in that it is not necessary to obtain the hysteresis
parameters by means of experiment, which relaxes time-consuming off-line
identification work.Moreover, an adaptive radial basis functions neural
network (RBFNN) is utilized to approximate the unknown disturbances,
whose weight coefficients along with parametric uncertainties are all
estimated by the adaptive technique. Besides, the communication cost can
be largely saved by introducing the relative threshold event-triggered
control (ETC). Through Lyapunov analysis, the proposed controller
guarantees the boundedness of all the signals and the convergence of the
error signals. The results of numerical simulation illustrate the
effectiveness and superiority of the developed controller.%
\end{abstract}%



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