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Based on Neural Network refining furnace electrode regulator Intelligent Control System

Author: LuFenLan
Tutor: RenJinXiaï¼›DuChengYang
School: Jiangxi University of Technology
Course: Control Engineering
Keywords: LF refining furnace Electrode lifting control BP neural network Intelligent Control
CLC: TP273
Type: Master's thesis
Year: 2011
Downloads: 31
Quote: 0
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Abstract


The existing ladle refining furnace electrode adjust by traditional control and three phase electrode lift control independently. Unbalanced three phase current, and run economic indicators are not ideal. When strong blowing argon current shock. Therefore, the control electrode lift is very important. Considering the LF refining furnace electrode regulating system is characteristic with a three-phase unbalanced typical, strong coupling, nonlinear and time-varying system, hoping to seek a suitable intelligent electrode adjust control method. Neural network can approximate any nonlinear function; Can be multiple input, multiple output; Easy to use the existing computer technology; Able to self learning, in order to adapt to changes in the environment, to simulate reality system complex relationship between input and output, is very suitable for LF refining furnace electrode control. So neural network is used to refining furnace electrode adjust control is a worthy of research direction. The author according to the actual operation data neural network system model is established, and applied the idea of decoupling control and neural network direct inverse control theory is established based on BP neural network inverse model controller and neural network direct inverse control network model. Then the electrode lifting the BP neural network model for training simulation. Results show that this is a kind of effective control method, can obtain good control quality.

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CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > Automation systems > Automatic control,automatic control system
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