Study on Glass Furance Temperature Dual Control Based on Neural Networks Decoupling Control under WINCC

Hao Ming Yang; Lan Qing Zhang

doi:10.4028/www.scientific.net/AMM.341-342.856

Paper Titles

A Simple and Long-Range Displacement Measurement System
p.839

Research on Fault Diagnosis Based on SVM
p.843

Hardware Design and Realization of Synthetical Altimeter Detecting-Instrument Based on Intellectronics
p.847

Research on FBG High Temperature Sensor Used for Strain Monitoring
p.851

Study on Glass Furance Temperature Dual Control Based on Neural Networks Decoupling Control under WINCC
p.856

Design and Realization of Intelligent LED Fluorescent Lighting Control System
p.861

A Simple and Rapid Electrochemical Method for Determination of Ascorbic Acid
p.866

Friction Torque Measurement of Servo Mount Based on Stribeck Model
p.870

Applications of Fuzz Comprehensive Evaluation in Display and Control System of Armored Vehicle
p.876

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 341-342Study on Glass Furance Temperature Dual Control...

Study on Glass Furance Temperature Dual Control Based on Neural Networks Decoupling Control under WINCC

Abstract:

Experiment control platform for the neural network decoupling control is constructed for the glass furnace taking heavy oil as fuel. By dual control, the improving Levenberg-Marquardt learning algorithm is discussed in order to improve the learning speed and to satisfy the real control. The neural network decoupling real control based on C-Script language and PLC S7-400 hard system under WINCC is realized with satisfying control results.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 341-342)

Pages:

856-860

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.341-342.856

Citation:

Cite this paper

Online since:

July 2013

Authors:

Hao Ming Yang, Lan Qing Zhang

Keywords:

Adaptive Inverse Control, Dual Control, Feedback Error Learning Algorithms, Glass Furnace, Neural Network (NN), WinCC

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Wu Baicheng. The Foundation of Glass manufacturing technology, Beijing: China Light Industry Press, (1997).

Google Scholar

[2] Weng Weiqin & Sun Hongcheng(2002). Process Control System and Automation, Beijing: Chemical industry press.

Google Scholar

[3] Zhang Lanqing, Dang Xuanju & Cheng Juanjuan. Dual-Control of Glass Furance Temperature Based on Dynamic Recurrent Neural Networks. Ordnance Industry Automation, 2007, 26(5), 67-69.

Google Scholar

[4] Tan Yonghong & Dang Xuanju. Integrated Intelligent Furance Temperature Control method of Glass Kiln. ZL200510020141. 0.

Google Scholar

[5] Muramatsu E，Watanabe K. Feedback error learning control without recourse to positive realness，Automatic Control，IEEE Transactions on, 2004，49（10）：1762-1770.

DOI: 10.1109/tac.2004.835399

Google Scholar

[6] Zhao Hong & Zhou Ruixiang, Neural Network Supervised Control Based on Levenberg-Marquardt Algorithm, " Journal of Xi, an Jiaotong University, 2002, 36(5): 523-527.

Google Scholar

[7] Basel AlAli, Kentaro Hirata and Kenji Sugimoto, Generalization of Feedback Error Learning (FEL) to Strictly Proper MIMO Systems, Proceedings of the 2006 American Control Conference Minneapolis, Minnesota, USA, (2006).

DOI: 10.1109/acc.2006.1657591

Google Scholar

[8] Chai Ruijuan & Chen Haixia. Siemens PLC programming technology and engineering application, China Machine Press, (2006).

Google Scholar