Study on Sampling Control in Hydraulic AGC Based on Neural Network

Author

Mei, Jian-Hong ; Wang, Hong-rui ; Xiao, Jin-zhuang ; Shan, Ming-Cai

Author_Institution

Hebei Univ., Baoding

Volume

1

fYear

2007

fDate

19-22 Aug. 2007

Firstpage

504

Lastpage

508

Abstract

This paper introduces a hydraulic automatic gauge control system for Hengtong 1270 mm cold mill in Tangshan, China. Identification and control are achieved based on RBF (radial basis function) and BP (back propagation) NN ( neural network) in APC (automatic position control) system. Sampling control is applied to solve the dead-time control problem because of the gauge´s location. Considering the inertia of the hydraulic servo-system, we propose a new method of selecting sample period. The simulation result and the application show that the control effect is satisfied, and this method can be used widely.

Keywords

backpropagation; control engineering computing; hydraulic control equipment; milling; position control; radial basis function networks; sampling methods; automatic position control system; back propagation neural network; cold mill; hydraulic automatic gauge control system; radial basis function; sampling control; Adaptive control; Artificial neural networks; Automatic control; Control systems; Machine learning; Milling machines; Neural networks; Nonlinear control systems; Position control; Sampling methods; Hydraulic AGC; Identification; Intelligent control; Neural network; Sample period; delay;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2007 International Conference on

Conference_Location

Hong Kong

Print_ISBN

978-1-4244-0973-0

Electronic_ISBN

978-1-4244-0973-0

Type

conf

DOI

10.1109/ICMLC.2007.4370197

Filename

4370197