Fuzzy neural network based generalized predictive control for AC excitation generators

2005 ◽  
Author(s):  
Shaosheng Fan ◽  
Yaonan Wang
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Fuzzy Neural Network ◽  
Generalized Predictive Control ◽  
Fuzzy Neural ◽  
Ac Excitation

The Application of Fuzzy Neural Network Generalized Predictive Control in Unit Plant Coordinated Control System

2014 ◽  
Vol 602-605 ◽  
pp. 1329-1331
Author(s):  
Shu Zhang ◽  
Hu Jun Ling ◽  
Zhen Lin Zhang ◽  
Meng Jie Hu ◽  
Yang Pang
Keyword(s):  
Neural Network ◽  
Mathematical Models ◽  
Predictive Control ◽  
Power Plants ◽  
Fuzzy Neural Network ◽  
Thermal Power ◽  
Coordinated Control ◽  
Steam Pressure ◽  
Generalized Predictive Control ◽  
Fuzzy Neural

The characteristic of unit coordinated control in thermal power plants having complex, nonlinear, larger time delay, and establishing mathematical models are very difficult. In the paper establish mathematical models of using fuzzy neural network system, make full use of the ability of fuzzy logic reasoning and neural network self-learning; using multivariable generalized predictive control strategy, Simulation results show that the use of fuzzy neural network generalized predictive control for good stability of main steam pressure , strong effectiveness of tracking the power grid load, and little fluctuation of different load conversion.

Research on tool control system of double cutters experimental platform based on fuzzy neural network predictive control

2021 ◽  
Vol 40 (1) ◽  
pp. 65-76
Author(s):  
Peng Zhou ◽  
Junxing Tian ◽  
Jian Sun ◽  
Jinmei Yao ◽  
Defang Zou ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Predictive Control ◽  
Hydraulic System ◽  
Fuzzy Neural Network ◽  
Fuzzy Controller ◽  
Control Signal ◽  
Control Technology ◽  
Fuzzy Neural ◽  
Neural Network Predictive Control

According to the characteristics of the tool hydraulic control system of the double cutters experimental pplatform, intelligent control methodology forecasted by fuzzy neural network is introduced into the control system. The two level control systems of fuzzy neural network predictive control and fuzzy control are designed. The fuzzy neural network predictive controller mainly completes the analysis and control of the speed and pressure in the tool hydraulic system. The speed control signal and pressure control signal from the first level are output to the fuzzy controller. Then, through logical reasoning, the control signal is output and the actuator is driven by the fuzzy controller to complete the control function of the tool system. In this paper, compared with the traditional PID control, the fuzzy neural network predictive control technology has better control accuracy, dynamic response performance and steady-state accuracy. The fuzzy neural network predictive control technology can be used to control the tool hydraulic system of Tunnel Boring Machine.

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