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The Appliance of Fuzzy Neural Networks in Process Control

Author: CaiWenXiu
Tutor: PanLiDeng
School: Beijing University of Chemical Technology
Course: Control Theory and Control Engineering
Keywords: Fuzzy logic neural network Fuzzy neural network PID control
CLC: TP183
Type: Master's thesis
Year: 2006
Downloads: 303
Quote: 4
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Abstract


Along with the development of technology there are complexity and uncertainty of control systems in industry manufacture process. Many uncertain factors, such as correlation, randomicity, will change incidentally when environment and time change. So traditional control technique based on mathematical model is ineffective. New control strategy has being seeking .Fuzzy control is a kind of human imitating technique which is independent on the controlled plant’s mathematical mode. It utilizes the knowledge and experience of experts to carry out rationalization. As a result, it has good robustness . But it is lack of the ability of self - learning or self-tuning in practical project. Neural network has the ability of self - learning in spite of its nonlinear mapping similar with Black-Box . The abilities of self-learning and expression of the whole system will be improved when they hand together . The research of this combination is in the ascendant.First, we analyze characteristics of the objects in the process control. Then the backgrounds , development and principles of fuzzy control, neural networks and fuzzy neural networks are introduced. Facing the characteristics in modern industry production , this paper bring forward a control strategy base on fuzzy RBF(radial basis function) neural network and the traditional PID controller. It tunes the parameters of the PID controller on line and improves the performance of the controller.Second, For working with the problem of badly precision of fuzzy controller , a method of neural-fuzzy controller is proposed. In order to realize the self-learning ability of a kind of PID-typed fuzzy controller designed only using two dimension rule base, stability and control precision of system are improved. The application of industrial process control shows the effectiveness of the scheme.

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