Adaptive Neural Control of Uncertain Nonstrict-Feedback Stochastic Nonlinear Systems with Output Constraint and Unknown Dead Zone

2017 ◽  
Vol 47 (8) ◽  
pp. 2048-2059 ◽  
Author(s):  
Hongyi Li ◽  
Lu Bai ◽  
Lijie Wang ◽  
Qi Zhou ◽  
Huanqing Wang
Keyword(s):  
Nonlinear Systems ◽  
Neural Control ◽  
Dead Zone ◽  
Stochastic Nonlinear Systems ◽  
Adaptive Neural Control ◽  
Output Constraint

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.

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