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
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;
Conference_Titel :
Control Conference (CCC), 2010 29th Chinese
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-6263-6