Backstepping-based decentralized bounded-H∞ adaptive neural control for a class of large-scale stochastic nonlinear systems

2019 ◽  
Vol 356 (15) ◽  
pp. 8049-8079
Author(s):  
Hui Liu ◽  
Xiaohua Li ◽  
Xiaoping Liu ◽  
Huanqing Wang
Keyword(s):  
Nonlinear Systems ◽  
Neural Control ◽  
Large Scale ◽  
Stochastic Nonlinear Systems ◽  
Adaptive Neural Control

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.

Adaptive neural control for a general class of pure-feedback stochastic nonlinear systems

2014 ◽  
Vol 135 ◽  
pp. 348-356 ◽  
Author(s):  
Huanqing Wang ◽  
Xiaoping Liu ◽  
Kefu Liu ◽  
Bing Chen ◽  
Chong Lin
Keyword(s):  
Nonlinear Systems ◽  
General Class ◽  
Neural Control ◽  
Stochastic Nonlinear Systems ◽  
Adaptive Neural Control
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