Adaptive neural tracking control for uncertain nonlinear systems with input and output constraints using disturbance observer

2017 ◽  
Vol 235 ◽  
pp. 27-37 ◽  
Author(s):  
Rong Li ◽  
Mou Chen ◽  
Qingxian Wu
2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Siyi Chen ◽  
Wei Liu ◽  
Huixian Huang

Aiming at the tracking control problem of a class of uncertain nonlinear systems, a nonsingular fast terminal sliding mode control scheme combining RBF network and disturbance observer is proposed. The sliding mode controller is designed by using nonsingular fast terminal sliding mode and second power reaching law to solve the problem of singularity and slow convergence in traditional terminal sliding mode control. By using the universal approximation of RBF network, the unknown nonlinear function of the system is approximated, and the disturbance observer is designed by using the hyperbolic tangent nonlinear tracking differentiator (TANH-NTD) to estimate the interference of the system and enhance the robustness of the system. The stability of the system is proved by the Lyapunov principle. The numerical simulation results show that the method can shorten the system arrival time, improve the tracking accuracy, and suppress the chattering phenomenon.


2018 ◽  
Vol 21 (1) ◽  
pp. 143-155 ◽  
Author(s):  
Hao An ◽  
Baris Fidan ◽  
Qianqian Wu ◽  
Changhong Wang ◽  
Xibin Cao

Sign in / Sign up

Export Citation Format

Share Document