Asymptotic Fuzzy Tracking Control for a Class of Stochastic Strict-Feedback Systems

2017 ◽  
Vol 25 (3) ◽  
pp. 556-568 ◽  
Author(s):  
Ci Chen ◽  
Zhi Liu ◽  
Yun Zhang ◽  
C. L. Philip Chen ◽  
Shengli Xie
Keyword(s):  
Tracking Control ◽  
Feedback Systems ◽  
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).

Command filtered backstepping tracking control of uncertain nonlinear strict-feedback systems under a directed graph

2016 ◽  
Vol 39 (7) ◽  
pp. 1027-1036 ◽  
Author(s):  
Yi Zhang ◽  
Guozeng Cui ◽  
Guangming Zhuang ◽  
Junwei Lu ◽  
Ze Li
Keyword(s):  
Directed Graph ◽  
Tracking Control ◽  
Design Procedure ◽  
Design Parameters ◽  
Feedback Systems ◽  
Consensus Tracking ◽  
Backstepping Approach ◽  
Non Linear ◽  
Distributed Controllers ◽  
Strict Feedback

This paper studies the distributed consensus tracking control problem of multiple uncertain non-linear strict-feedback systems under a directed graph. The command filtered backstepping approach is utilised to alleviate computation burdens and construct distributed controllers, which involves compensated signals eliminating filtered error effects in the design procedure. Neural networks are employed to estimate uncertain non-linear items. Using a Lyapunov stability theorem, it is proved that all signals in the closed-looped systems are semi-globally uniformly ultimately bounded. In addition, consensus errors converge to a small neighbourhood of the origin by adjusting the appropriate design parameters. Finally, simulation results are presented to demonstrate the effectiveness of the developed control design approach.

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