Study of PID Neural Network Control for Nonlinear System

2006 ◽  
Author(s):  
Ming-guang Zhang ◽  
Ming-hui Qiang
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Network Control ◽  
Neural Network Control ◽  
Pid Neural Network

Study on Multivariable System Based on PID Neural Network Control

2012 ◽  
Vol 591-593 ◽  
pp. 1490-1495 ◽  
Author(s):  
Huai Lin Shu ◽  
Jin Tian Hu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Transfer Function ◽  
Design Process ◽  
Network Control ◽  
Neural Network Control ◽  
Single Variable ◽  
Multivariable System ◽  
Pid Neural Network ◽  
Self Learning

The multivariable PID neural network (MPIDNN) control system is introduced in this paper. MPIDNN is used to perform both the control and the decouple at the same time and to get better performance. It is difficult to control multivariable system by conventional controller because the strong coupling properties of the system. Generally, the decoupling system should be designed first and the multivariable object would be divided into several single variable objects. Then, several simple controller would achieve the control of those objects. The decoupling system and the controller exist in theory but the design process is very difficult actually because the transfer function of the object is difficult to get. Especially, if the number of the object inputs is not equal to that of the object outputs, which is called unsymmetry object, the conventional decoupling is impossible. A actual example is discussed in the paper in order to prove the function of the MPIDNN, in which an un-symmetry multivariable system which has 3 inputs and 2 outputs is controlled by a MPIDNN and the perfect control property is obtained by self-learning process.

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