Adaptive Neural Network Learning Controller Design for a Class of Nonlinear Systems With Time-Varying State Constraints

2020 ◽  
Vol 31 (1) ◽  
pp. 66-75 ◽  
Author(s):  
Yan-Jun Liu ◽  
Lei Ma ◽  
Lei Liu ◽  
Shaocheng Tong ◽  
C. L. Philip Chen
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
State Constraints ◽  
Controller Design ◽  
Time Varying ◽  
Adaptive Neural Network ◽  
Neural Network Learning ◽  
Network Learning

scholarly journals Research on Adaptive Neural Network Control System Based on Nonlinear U-Model with Time-Varying Delay

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Fengxia Xu ◽  
Yao Cheng ◽  
Hongliang Ren ◽  
Shili Wang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Delay System ◽  
Network Control ◽  
Identification Algorithm ◽  
Time Varying ◽  
Adaptive Neural Network ◽  
Time Varying Delay ◽  
Adaptive Neural Network Control ◽  
Varying Delay

U-model can approximate a large class of smooth nonlinear time-varying delay system to any accuracy by using time-varying delay parameters polynomial. This paper proposes a new approach, namely, U-model approach, to solving the problems of analysis and synthesis for nonlinear systems. Based on the idea of discrete-time U-model with time-varying delay, the identification algorithm of adaptive neural network is given for the nonlinear model. Then, the controller is designed by using the Newton-Raphson formula and the stability analysis is given for the closed-loop nonlinear systems. Finally, illustrative examples are given to show the validity and applicability of the obtained results.

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