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).

scholarly journals Partial-State-Constrained Adaptive Intelligent Tracking Control of Nonlinear Nonstrict-Feedback Systems with Unmodeled Dynamics and Its Application

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yuzhuo Zhao ◽  
Ben Niu ◽  
Xiaoli Jiang ◽  
Ping Zhao ◽  
Huanqing Wang ◽  
...  
Keyword(s):  
Tracking Control ◽  
Tracking Error ◽  
Small Neighborhood ◽  
Unmodeled Dynamics ◽  
Feedback Systems ◽  
Feedback Form ◽  
Design Algorithm ◽  
Adaptive Tracking Control ◽  
Control Scheme ◽  
Nonlinear Uncertainties

In this paper, an adaptive intelligent control scheme is presented to investigate the problem of adaptive tracking control for a class of nonstrict-feedback nonlinear systems with constrained states and unmodeled dynamics. By approximating the unknown nonlinear uncertainties, utilizing Barrier Lyapunov functions (BLFs), and designing a dynamic signal to deal with the constrained states and the unmodeled dynamics, respectively, an adaptive neural network (NN) controller is developed in the frame of the backstepping design. In order to simplify the design process, the nonstrict-feedback form is treated by using the special properties of Gaussian functions. The proposed adaptive control scheme ensures that all variables involved in the closed-loop system are bounded, the corresponding state constraints are not violated. Meanwhile, the tracking error converges to a small neighborhood of the origin. In the end, the proposed intelligent design algorithm is applied to one-link manipulator to demonstrate the effectiveness of the obtained method.

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