Recurrent fuzzy neural network controller design using sliding-mode control for linear synchronous motor drive

2004 ◽  
Vol 151 (4) ◽  
pp. 407-416 ◽  
Author(s):  
F.-J. Lin ◽  
P.-K. Huang ◽  
C.-H. Lin
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Fuzzy Neural Network ◽  
Controller Design ◽  
Sliding Mode ◽  
Motor Drive ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Recurrent Fuzzy Neural Network

Adaptive asymmetric fuzzy neural network controller design via network structuring adaptation

2008 ◽  
Vol 159 (20) ◽  
pp. 2627-2649 ◽  
Author(s):  
Chun-Fei Hsu ◽  
Ping-Zong Lin ◽  
Tsu-Tian Lee ◽  
Chi-Hsu Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Controller Design ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural

Aviation Flight Attitude Control Technology

2014 ◽  
Vol 716-717 ◽  
pp. 1567-1571
Author(s):  
Jun Hu
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Sliding Mode ◽  
Gaussian White Noise ◽  
Training Data ◽  
External Interference ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
The Impact

This paper presents a range of two miniature spacecraft attitude Fuzzy Neural Network Sliding adaptive controller, using a weighting factor to combine indirect and direct fuzzy neural network controller fuzzy neural network controller. Interval two free parameters of fuzzy neural network adaptive sliding mode controller via output feedback control law and adaptive law be adjusted online. Simulation results show that after joining 10db Gaussian white noise, and in order to reduce the impact of external interference and noise training data, a controller with respect to the range of large amount of control type II generated by the controller. Overall adaptive scheme guarantees the global stability of the closed-loop system, all signals involved are bounded in some way, and also showed a high level of tracking performance.

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