PID neural network control of hydraulic roll gap control system

2012 ◽  
Author(s):  
Jing Zhang ◽  
Yutao Fan ◽  
Weifeng Zhong ◽  
Junshan Gao ◽  
Tingting Guan ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Network Control ◽  
Neural Network Control ◽  
Gap Control ◽  
Roll Gap ◽  
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.

The Electro-Hydraulic Proportional Control and Simulation of the Radial Piston Pump Based on Proportional-Integral-Derivative Neural Network

2010 ◽  
Vol 139-141 ◽  
pp. 1749-1752
Author(s):  
Lan Li ◽  
Jiang Ye ◽  
Xue Fei Zheng
Keyword(s):  
Neural Network ◽  
Control System ◽  
Pid Control ◽  
Control Algorithm ◽  
Network Control ◽  
Piston Pump ◽  
System Response ◽  
Neural Network Control ◽  
Proportional Control ◽  
Pid Neural Network

In this paper a new control method has been studied in which PID control system was integrated into the neural network. It could overcome some disadvantages such as neural network’s slow rate of convergence and PID’s difficulty in application of multivariate nonlinear systems. A controller of the Electro-hydraulic proportional control stroking mechanism for radial piston pump was designed based on the PID neural network control algorithm. The system responses of system variable control signal of system track were achieved by computer simulation. It was found by PIDNN that the control system could reach steady state in a shorter time, compared with PID control system response time by 65% to 80%. The simulation results showed that the controller for the Electro-hydraulic proportional Radial Piston Pump based PID neural network control algorithm would have a good controlling performance.

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