Abstract
In this paper, the dynamic event-triggered tracking control issue is studied for a class of unknown stochastic nonlinear systems with strict-feedback form. At first, neural networks (NNs) are used to approximate the unknown nonlinear functions. Then, a dynamic event-triggered controller (DETC) is designed through the adaptive backstepping method. Especially, the triggered threshold is dynamically adjusted. Compared with its corresponding static event-triggered mechanism (SETM), the dynamic event-triggered mechanism (DETM) can generate a larger execution interval and further save resources. Moreover, it is verified by two simulation examples that show that the closed-loop stochastic system signals are ultimately fourth moment semi-globally uniformly bounded (SGUUB).
Highlights
In actual engineering applications, network bandwidth is usually limited, and there exist multiple devices sharing the same network
(2) An adaptive controller with dynamic event-triggered mechanism (DETM) is designed for unknown stochastic nonlinear strict-feedback systems
For a class of unknown stochastic nonlinear strict-feedback systems, a dynamic eventtriggered design method is investigated in this paper
Summary
Network bandwidth is usually limited, and there exist multiple devices sharing the same network. For a class of unknown stochastic nonlinear strict-feedback systems, an adaptive DETC is designed in combination with NNs [31,32,33,34,35,36,37,38,39]. (1) A class of unknown stochastic nonlinear strict-feedback systems are considered in this paper, which differ from the deterministic systems studied in [28]. (2) An adaptive controller with DETM is designed for unknown stochastic nonlinear strict-feedback systems. The notations of this paper is explained as follows: Rn×r , Rn , and R represent the sets of real n × r matrix space, real n-dimensional vector space, and real numbers, respectively.
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