DocumentCode
2107428
Title
Adaptive ANN modeling of proportional valve based on data classification
Author
Xiao Qiao ; Wang Shoukun ; Wang Junzheng
Author_Institution
Sch. of Autom., Beijing Inst. of Technol., Beijing, China
fYear
2010
fDate
29-31 July 2010
Firstpage
1352
Lastpage
1357
Abstract
This paper aims at controlling the nonlinear couple variables voltage and flow precisely in the electro-hydraulic proportional. A static model of voltage, pressure and flow, which is established from BP neural network based on data classification, is presented. The data classification principle is given based on dead zone and hysteresis which may cause the low accuracy of model. The experimental results and applications show that the modeling can reflect the characteristics of electro-hydraulic proportional and achieve high precision in pressure controlling. What is more, Depending on real time online parameters tuning, it can improve the model accuracy and control precision.
Keywords
adaptive control; backpropagation; electrohydraulic control equipment; flow control; neurocontrollers; nonlinear control systems; pressure control; valves; voltage control; BP neural network; adaptive ANN modeling; data classification; dead zone; electro-hydraulic proportional; flow control; hysteresis; nonlinear modeling; pressure control; proportional valve; real time online parameters tuning; static model; voltage control; Adaptation model; Artificial neural networks; Data models; Valves; Variable speed drives; Adaption; BP Neural Network; Data Classification; Electro-hydraulic Proportional; Nonlinear Modeling;
fLanguage
English
Publisher
ieee
Conference_Titel
Control Conference (CCC), 2010 29th Chinese
Conference_Location
Beijing
Print_ISBN
978-1-4244-6263-6
Type
conf
Filename
5573416
Link To Document