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\begin{document}

\title{Integral Sliding Mode-Based Event-Triggered Nearly Optimal Tracking
Control for Uncertain Nonlinear Systems}



\author[1]{Yongwei Zhang}%
\author[2]{Shunchao Zhang}%
\author[1]{Yonghua Wang}%
\author[2]{Dacai Liu}%
\author[1]{Jiawei Zhuang}%
\affil[1]{Guangdong University of Technology School of Automation}%
\affil[2]{Guangdong University of Finance}%


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  \date{May 26, 2024}


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\begin{abstract}
In this paper, an event-triggered nearly optimal tracking control method
is investigated for a class of uncertain nonlinear systems by
integrating adaptive dynamic programming (ADP) and integral sliding mode
(ISM) control. By introducing a neural network (NN) adaptive term, the
designed ISM-based discontinuous control law is employed to eliminate
the influence of the uncertainties and obtain the tracking error system
constructed from the sliding mode dynamics, as well as relax the known
upper-bounded condition of uncertainties. In order to guarantee the
stability of tracking error system and improve the control performance,
under the ADP technique, a critic NN is applied to approximate the
optimal value function for solving the event-triggered
Hamilton-Jacobi-Bellman equation and the event-triggered nearly optimal
feedback control is obtained. The feedback control law is updated and
transmitted to plant only when events occur, thus both the communication
and the computational resources can be saved. Furthermore, the stability
of tracking error is proven thanks to Lyapunov's direct method. Finally,
we provide two simulation examples to validate the developed control
scheme.%
\end{abstract}%



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