Adaptive tracking control for a class of nonlinear non-strict-feedback systems

2017 ◽  
Vol 88 (3) ◽  
pp. 1537-1550 ◽  
Author(s):  
Qiu-Ni Li ◽  
Ren-Nong Yang ◽  
Zong-Cheng Liu
Keyword(s):  
Tracking Control ◽  
Feedback Systems ◽  
Adaptive Tracking Control ◽  
Adaptive Tracking ◽  
Strict Feedback

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

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1648
Author(s):  
Yingying Fu ◽  
Jing Li ◽  
Shuiyan Wu ◽  
Xiaobo Li
Keyword(s):  
Tracking Control ◽  
Stochastic Nonlinear Systems ◽  
Fourth Moment ◽  
Feedback Systems ◽  
Nonlinear Functions ◽  
Feedback Form ◽  
Adaptive Tracking Control ◽  
Dynamic Event ◽  
Event Triggered ◽  
Strict Feedback

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).

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