Research and Experiment of Pneumatic Servo System Based on Neural Network PID Control

2006 ◽  
Author(s):  
Kailong Cai ◽  
Shousheng Xie ◽  
Yong Wu
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Servo System ◽  
Pneumatic Servo System ◽  
Neural Network Pid

Analysis of Speed Servo System of Pneumatic Manipulator Based RBF Neural Network PID Control

2010 ◽  
Vol 40-41 ◽  
pp. 65-70 ◽  
Author(s):  
Jing Luo ◽  
Rui Bo Yuan ◽  
Yu Bi Yuan ◽  
Shao Nan Ba ◽  
Zong Cheng Zhang
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Hybrid Control ◽  
Rbf Neural Network ◽  
Servo System ◽  
Fast Response ◽  
Quick Response ◽  
Pneumatic Servo System ◽  
The Stability ◽  
Neural Network Pid

Through analysis and comparison of simple PID control and RBF neural network-PID hybrid control of the pneumatic servo system, then compared the stability and quick response under the two control system. Concluded that RBF neural network-PID hybrid control has better stability and fast response than the simple PID control.

Neural Network PID Control Application of Hydraulic position Servo System for Precision Improvement

2014 ◽  
Author(s):  
Lihua Liang ◽  
Mingxiao Sun ◽  
Songtao Zhang ◽  
Yu Wen ◽  
Peng` Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Servo System ◽  
Position Servo ◽  
Control Application ◽  
Neural Network Pid

Control of Pneumatic Servo System Based on Neural Network PID Algorithm

2013 ◽  
Vol 457-458 ◽  
pp. 1344-1347 ◽  
Author(s):  
Qin Hui Gong
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Learning Rule ◽  
Servo System ◽  
External Disturbances ◽  
Pid Algorithm ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Pneumatic Servo System ◽  
Adaptive Ability

The pneumatic servo system has characteristics of nonlinear, time-variant, large parameter variations and external disturbances, which is difficult to control. The conventional PID control is not suitable for the variable parameters of the controlled object, external disturbances. In this paper, the neural network controller combined with PID control is used to control the pneumatic servo system, and the structure diagram, algorithm and learning rule of the single neuron adaptive PID controller are put forward. The results show that,compared with the traditional PID control, the controller has significantly improved the control performance of system, Namely, the system has faster computational speed (real-time), stronger robustness and better adaptive ability.

Application of BP Neural Network PID Algorithm in Refrigerated Space Temperature Control

2014 ◽  
Vol 599-601 ◽  
pp. 827-830 ◽  
Author(s):  
Wei Tian ◽  
Yi Zhun Peng ◽  
Pan Wang ◽  
Xiao Yu Wang
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Bp Neural Network ◽  
Pid Control ◽  
Bp Algorithm ◽  
Pid Algorithm ◽  
Controller Performance ◽  
Neural Network Algorithm ◽  
Adaptive Ability ◽  
Neural Network Pid

Taking the temperature control of a refrigerated space as example, this paper designs a controller which is based on traditional PID operation and BP neural network algorithm. It has better steady-state precision and adaptive ability. Firstly, the article introduces the concepts of the refrigerated space, PID and BP algorithm. Then, the temperature control of refrigerated space is simulated in MATLAB. The PID parameters will be adjusted by simulation in BP Neural Network. The PID control parameters could be created real-time online, which makes the controller performance best.

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