Identification for hydraulic AGC system of strip mill based on neural networks

Author

Wang, Haifang ; Rong, Yu ; Liu, Shengtao ; Cui, Jinhua

Author_Institution

Coll. of Mech. & Electron. Eng., Hebei Normal Univ. Sci. & Technol., Qinhuangdao, China

Volume

2

fYear

2010

fDate

25-27 June 2010

Abstract

A new adaptive identification method is presented based on analyzing the dynamic peculiarities of the components in the nonlinear hydraulic automatic gauge control press system of strip mill. A feed-forward and dynamic neural network structure is built based on the time series using enlarged back-propagation algorithm, and the nonlinear performance of press control system of the hydraulic automatic gauge control system can be forecasted. Based on the forecasted results, the characteristic parameters of linear system are identified by least square method. Finally, the applicability of the adaptive identification method is illustrated and verified by simulation results.

Keywords

backpropagation; feedforward neural nets; gauges; hydraulic actuators; identification technology; least squares approximations; rolling mills; adaptive identification method; backpropagation algorithm; dynamic neural network structure; feedforward neural network structure; hydraulic AGC system identification; least square method; linear system; nonlinear hydraulic automatic gauge control press system; press control system; strip mill; time series; Adaptive control; Adaptive systems; Automatic control; Control systems; Milling machines; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Programmable control; Strips; AGC; BP algorithm; identification; least square; neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Design and Applications (ICCDA), 2010 International Conference on

Conference_Location

Qinhuangdao

Print_ISBN

978-1-4244-7164-5

Electronic_ISBN

978-1-4244-7164-5

Type

conf

DOI

10.1109/ICCDA.2010.5541406

Filename

5541406