Modeling of Boiler-Turbine Nonlinear Coordinated Control System Based on RBF Neural Network

2007 ◽  
Author(s):  
Daogang Peng ◽  
Hao Zhang ◽  
Ping Yang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Rbf Neural Network ◽  
Coordinated Control

Coordinated Control System Modeling of Ultra-Supercritical Unit based on a New Fuzzy Neural Network

Energy ◽  
2021 ◽  
pp. 121231
Author(s):  
Guolian Hou ◽  
Jian Xiong ◽  
Guiping Zhou ◽  
Linjuan Gong ◽  
Congzhi Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Neural Network ◽  
System Modeling ◽  
Coordinated Control ◽  
Fuzzy Neural

scholarly journals Fuzzy Neural Network Q-Learning Method for Model Disturbance Change: A Deployable Antenna Panel Application

2019 ◽  
Vol 2019 ◽  
pp. 1-21
Author(s):  
Zhiyong Liu ◽  
Hong Bao ◽  
Song Xue ◽  
Jingli Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Basis Function ◽  
Fuzzy Neural Network ◽  
Rbf Neural Network ◽  
Learning Method ◽  
Active Deformation ◽  
Q Learning ◽  
Adjustment Mechanism ◽  
Fuzzy Neural

This paper addresses the disturbance change control problem with an active deformation adjustment mechanism on a 5-meter deployable antenna panel. A fuzzy neural network Q-learning control (FNNQL) strategy is proposed in this paper for the disturbance change to improve the accuracy of the antenna panel. In the proposed method, the error of the model disturbance is reduced by introducing the fuzzy radial basis function (RBF) neural network into Q-learning, and the parameters of the fuzzy RBF neural network were optimized and adjusted by a Q-learning method. This allows the FNNQL controller to have a strong adaptability to deal with the disturbance change. Finally, the proposed method has been adopted in the middle plate of a 5-meter deployable antenna panel, and it was found that the method could successfully adapt the model disturbance change in the antenna panel. Results of the simulation also show that the whole control system meets the required accuracy requirements.

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