Dynamic Event-Triggered Adaptive Tracking Control for a Class of Unknown Stochastic Nonlinear Strict-Feedback Systems

• Main Authors: Yingying Fu, Jing Li, Shuiyan Wu, Xiaobo Li
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Symmetry
• Subjects: stochastic nonlinear system; dynamic event-triggered controller; neural network; adaptive backstepping
• Online Access: https://www.mdpi.com/2073-8994/13/9/1648

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).