A Dynamic Fuzzy Neural Networks-Based Surface Vessels Course Tracking Controller

2014 ◽  
Vol 644-650 ◽  
pp. 122-127
Author(s):  
Yang Wang ◽  
Li Li Guo ◽  
Chen Guo
Keyword(s):  
Neural Networks ◽  
Tracking Control ◽  
Pid Controller ◽  
Fuzzy Neural Networks ◽  
Control Methods ◽  
Intelligent Algorithm ◽  
Surface Vessels ◽  
Tracking Controller ◽  
Fuzzy Neural ◽  
Simulation Results

A Dynamic Fuzzy Neural Networks Course Tracking Controller (DFNNCTC) for Surface Vessels is presented to solve the uncertainties coursing by the wide and wave. A Dynamic Fuzzy Neural Networks (DFNN) combines with a PID controller to integrate the DFNNCTC, in which the structure and parameters are adjusted online, and the fuzzy rules are automatically generated when being trained. The intelligent algorithm conquers the disadvantage of either overfitting or overtraining in traditional static fuzzy neural networks-based control methods. Simulation results of a container’s course tracking control validate the effectiveness of the proposed algorithm.

Fuzzy Neural Networks Used in PID Controller of the Secondary Regulation Speed System

2010 ◽  
Vol 145 ◽  
pp. 20-25
Author(s):  
Min Dong ◽  
Guo You Li ◽  
Ling Feng Li
Keyword(s):  
Neural Networks ◽  
Pid Controller ◽  
Mathematic Model ◽  
Fuzzy Neural Networks ◽  
Time Variability ◽  
Simulation Research ◽  
System A ◽  
Fuzzy Neural ◽  
New Type ◽  
Simulation Results

Aiming at the control of the secondary regulation speed control system, A new-type of fuzzy neural networks is used in the parameters setting of PID controller, which is helpful to overcome the uncertainty, nonlinearity and time-variability of the system. By establishing the mathematic model of the system, Simulation research are carried out. Simulation results indicate that the fuzzy neural networks model improves the dynamic response of the system.

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