Robust Sugeno type adaptive fuzzy neural network backstepping control for two-axis motion control system

2008 ◽  
Author(s):  
Faa-Jeng Lin ◽  
Po-Huan Chou ◽  
Po-Hung Shen
Keyword(s):  
Neural Network ◽  
Control System ◽  
Motion Control ◽  
Fuzzy Neural Network ◽  
Backstepping Control ◽  
Motion Control System ◽  
Adaptive Fuzzy Neural Network ◽  
Adaptive Fuzzy ◽  
Fuzzy Neural

scholarly journals Command Filtered Adaptive Fuzzy Neural Network Backstepping Control for Marine Power System

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Xin Zhang ◽  
Longhua Mu
Keyword(s):  
Neural Network ◽  
Power System ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Chaotic Oscillation ◽  
Lyapunov Stability Theory ◽  
Backstepping Control ◽  
Adaptive Fuzzy Neural Network ◽  
Adaptive Fuzzy ◽  
Fuzzy Neural

In order to retrain chaotic oscillation of marine power system which is excited by periodic electromagnetism perturbation, a novel command-filtered adaptive fuzzy neural network backstepping control method is designed. First, the mathematical model of marine power system is established based on the two parallel nonlinear model. Then, main results of command-filtered adaptive fuzzy neural network backstepping control law are given. And the Lyapunov stability theory is applied to prove that the system can remain closed-loop asymptotically stable with this controller. Finally, simulation results indicate that the designed controller can suppress chaotic oscillation with fast convergence speed that makes the system return to the equilibrium point quickly; meanwhile, the parameter which induces chaotic oscillation can also be discriminated.

Application of type-II fuzzy neural network to adaptive double axis motion control system

2011 ◽  
Vol 19 (7) ◽  
pp. 1643-1650 ◽  
Author(s):  
陈向坚 CHEN Xiang-jian ◽  
李迪 LI Di ◽  
白越 BAI Yue ◽  
续志军 XU Zhi-jun
Keyword(s):  
Neural Network ◽  
Control System ◽  
Motion Control ◽  
Fuzzy Neural Network ◽  
Type Ii ◽  
Motion Control System ◽  
Fuzzy Neural

Robust control for permanent magnet in-wheel motor in electric vehicles using adaptive fuzzy neural network with inverse system decoupling

2018 ◽  
Vol 42 (3) ◽  
pp. 286-297 ◽  
Author(s):  
Yong Li ◽  
Bohan Zhang ◽  
Xing Xu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Permanent Magnet ◽  
Fuzzy Neural Network ◽  
Speed Control ◽  
Inverse System ◽  
Adaptive Fuzzy Neural Network ◽  
Adaptive Fuzzy ◽  
Speed Control System ◽  
Fuzzy Neural

To eliminate the chattering phenomenon and effectively enhance the robustness and dynamic response of the speed control system of a permanent magnet in-wheel motor (PMIWM), a novel decoupling approach is proposed. The speed control system of the PMIWM is analyzed and modeled. By introducing the inverse model into the original PMIWM system, a new decoupling pseudo-linear system is established. A control method based on adaptive fuzzy neural network (AFNN) is investigated to obtain an accurate speed trajectory. The inverse system control approach is introduced into the AFNN-based control system. The PMIWM speed is decoupled completely by the proposed adaptive fuzzy neural network inverse (AFNNI) method. Experiments are carried out on a hardware-in-the-loop (HIL) test bench. Compared with traditional PID control scheme, the proposed AFNNI control strategy can realize a better speed control performance and ensure the robust stability of the PMIWM, even though the motor may suffer from both sudden change in velocity and severe variation under different drive cycles.

Sign in / Sign up

Export Citation Format

Share Document