Adaptive Neural Tracking Control for Switched High-Order Stochastic Nonlinear Systems

2017 ◽  
Vol 47 (10) ◽  
pp. 3088-3099 ◽  
Author(s):  
Xudong Zhao ◽  
Xinyong Wang ◽  
Guangdeng Zong ◽  
Xiaolong Zheng
Keyword(s):  
Nonlinear Systems ◽  
Tracking Control ◽  
High Order ◽  
Stochastic Nonlinear Systems

scholarly journals The Adaptive Neural Control for a Class of High-Order Uncertain Stochastic Nonlinear Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Xiaoyan Qin
Keyword(s):  
Nonlinear Systems ◽  
Neural Control ◽  
Closed Loop ◽  
Control Method ◽  
High Order ◽  
Stochastic Nonlinear Systems ◽  
Closed Loop System ◽  
Adaptive Neural Control ◽  
Control Scheme ◽  
Approximation Capability

This paper studies the problem of the adaptive neural control for a class of high-order uncertain stochastic nonlinear systems. By using some techniques such as the backstepping recursive technique, Young’s inequality, and approximation capability, a novel adaptive neural control scheme is constructed. The proposed control method can guarantee that the signals of the closed-loop system are bounded in probability, and only one parameter needs to be updated online. One example is given to show the effectiveness of the proposed control method.

Output tracking control of high-order nonlinear systems with dynamic uncertainties

2019 ◽  
Vol 42 (8) ◽  
pp. 1511-1520
Author(s):  
Zong-Yao Sun ◽  
Yu-Jie Gu ◽  
Qinghua Meng ◽  
Wei Sun ◽  
Zhen-Guo Liu
Keyword(s):  
Nonlinear Systems ◽  
Tracking Control ◽  
Tracking Error ◽  
High Order ◽  
Approximation Technique ◽  
Zero Dynamic ◽  
Adding A Power Integrator ◽  
Output Tracking ◽  
Output Tracking Control ◽  
Dynamic Uncertainties

This paper investigates the output tracking control problem for a class of nonlinear systems with zero dynamic. On the basis of adding a power integrator method and approximation technique, an appropriate controller is proposed to guarantee that the tracking error turns to a preassigned neighborhood of the origin. The systems under investigation allow unmeasurable dynamic uncertainties, unknown nonlinear functions and unknown high-order terms. As an application, two examples are provided to illustrate the effectiveness of a control strategy.

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