Adaptive Predictive Control System with Disturbance Compensation based on Self-recurrent Wavelet Neural Network

2011 ◽  
Vol 3 (10) ◽  
pp. 330-338
Author(s):  
Da Liu ◽  
Muguo Li
Keyword(s):  
Neural Network ◽  
Control System ◽  
Predictive Control ◽  
Wavelet Neural Network ◽  
Disturbance Compensation ◽  
Adaptive Predictive Control

Discrete-time adaptive predictive control system by 2-port design

1989 ◽  
Vol 20 (3) ◽  
pp. 443-455
Author(s):  
HSIAO-PING HUANG ◽  
HSIEN-SHENG LIN ◽  
YUNG-CHENG CHAO
Keyword(s):  
Control System ◽  
Predictive Control ◽  
Discrete Time ◽  
Port Design ◽  
Adaptive Predictive Control

scholarly journals Analysis and Application of intelligent Power Control System Cyber Security Situation Awareness Based on Wavelet Neural Network

2021 ◽  
Vol 2078 (1) ◽  
pp. 012067
Author(s):  
Jingcheng Zhao ◽  
Xiaomeng Li ◽  
Yaofu Cao ◽  
Junwen Liu ◽  
Junlu Yan ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Network Security ◽  
Power Control ◽  
Situation Awareness ◽  
Wavelet Neural Network ◽  
Control Network ◽  
Industrial Control ◽  
True Value ◽  
Power Control System

Abstract In recent years, international industrial control network security incidents have occurred frequently. As a core component of the industrial control field, intelligent power control systems are increasingly threatened by external network attacks. Based on the current research status of power industrial control network security, closely combining the development of active monitoring and defense technology in the public network field and the problems encountered by network security operators in actual work, this paper uses data mining methods to study the power control system network security situation awareness technology. Combing operational data collection and integrated processing, situation index screening and extraction, we use wavelet neural network analysis method to train the sampled data set, and finally calculate the true value of the network security status through deep intelligent learning. Finally, we conclude that the artificial intelligence algorithm based on wavelet neural network can be used for power control system network security situation awareness. In actual work, it can predict the situation value for a period of time in the future and assist network security personnel in judgment and decision-making.

PID Parameters Tuning Based on Wavelet Neural Network for Automatic Voltage Regulator Control System

2012 ◽  
Vol 463-464 ◽  
pp. 1663-1667
Author(s):  
Hai Na Hu ◽  
Wu Wang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Power System ◽  
Synchronous Generator ◽  
Wavelet Neural Network ◽  
System Stability ◽  
Voltage Regulator ◽  
System Control ◽  
Automatic Voltage Regulator ◽  
Avr System

Automatic Voltage Regulator (AVR) was applied to hold terminal voltage magnitude of a synchronous generator at a specified level and its stability seriously affects the security of power system. PID control was applied for AVR system control, but the parameters of PID controller were hard to determine, to overcome this problem, some intelligent techniques should be taken. Wavelet Neural Network (WNN) was constrictive and fluctuant of wavelet transform and has self-study, self adjustment and nonlinear mapping functions of neural networks, so the structure of WNN and PID tuning with WNN was proposed, the tuning algorithm was applied into AVR control system, the simulation was taken with normal BP neural network and WNN, the efficiency and advantages of this control strategy was successfully demonstrated which can applied into AVR system for power system stability.

Hot Blast Stove Temperature Control System Based on Neural Network Predictive Control

2014 ◽  
Vol 709 ◽  
pp. 281-284 ◽  
Author(s):  
Yao Wu Tang ◽  
Xiang Liu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Predictive Control ◽  
Control Method ◽  
Delay System ◽  
Temperature Control System ◽  
Control Effect ◽  
Rolling Optimization ◽  
Hot Blast ◽  
Neural Network Predictive Control

Chain type coal-fired hot blast furnace boiler has a strong coupling, large delay, large inertia characteristics. Control effect of control method of mathematic modeling method and the classical routine of it is very difficult to produce the ideal. The predictive control theory combined with neural network theory. Through the model correction and rolling optimization control method of the system is good to overcome the effects of model error and time-varying process. The experimental results showed that neural network predictive control system is improved effectively the static precision and dynamic characteristic. It has better practicability of boiler temperature of this kind of large time delay system.

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