Share This Article:

Strip Thickness Control of Cold Rolling Mill with Roll Eccentricity Compensation by Using Fuzzy Neural Network

Full-Text HTML XML Download Download as PDF (Size:733KB) PP. 27-33
DOI: 10.4236/eng.2014.61005    7,916 Downloads   10,871 Views   Citations

ABSTRACT

In rolling mill, the accuracy and quality of the strip exit thickness are very important factors. To realize high accuracy in the strip exit thickness, the Automatic Gauge Control (AGC) system is used. Because of roll eccentricity in backup rolls, the exit thickness deviates periodically. In this paper, we design PI controller in outer loop for the strip exit thickness while PD controller is used in inner loop for the work roll actuator position. Also, in order to reduce the periodic thickness deviation, we propose roll eccentricity compensation by using Fuzzy Neural Network with online tuning. Simulink model for the overall system has been implemented using MATLAB/SIMULINK software. The simulation results show the effectiveness of the proposed control.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

W. Hameed and K. Mohamad, "Strip Thickness Control of Cold Rolling Mill with Roll Eccentricity Compensation by Using Fuzzy Neural Network," Engineering, Vol. 6 No. 1, 2014, pp. 27-33. doi: 10.4236/eng.2014.61005.

References

[1] G. Zheng and Q.-Q. Qu, “Research on Periodical Fluctuations Identification and Compensation Control Method for Export Thickness in Rolling Mill,” IEEE Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, Baoding, 12-15 July 2009, pp. 1972-1977.
[2] K. M. Takami, J. Mahmoudi and E. Dahlquist, “Adaptive Control of Cold Rolling System in Electrical Strips Production System with Online-Offline Predictors,” Springer International Journal of Advanced Manufacturing Technology, Vol. 50, No. 9, 2010, pp. 917-930.
http://dx.doi.org/10.1007/s00170-010-2585-7
[3] G. Hwang, H.-S. Ahn, D.-H. Kim, T.-W. Yoon, S.-R. Oh and K.-B. Kim, “Design of a Robust Thickness Controller for a Single-Stand Cold Rolling Mill,” IEEE Proceedings of the International Conference on Control Applications, Dearborn, 15-18 September 1996, pp. 468-473.
[4] Mishra, et al., “Design of Hybrid Fuzzy Neural Network for Function Approximation,” Journal of Intelligent Learning Systems and Applications, Vol. 2, No. 2, 2010, pp. 97-109.
http://dx.doi.org/10.4236/jilsa.2010.22013
[5] K. Naga Sujatha and K. Vaisakh, “Implementation of Adaptive Neuro Fuzzy Inference System in Speed Control of Induction Motor Drives,” Journal of Intelligent Learning Systems and Applications, Vol. 2, No. 2, 2010, pp. 110-118. http://dx.doi.org/10.4236/jilsa.2010.22014
[6] J. Pittner and M. A. Simaan, “Tandem Cold Metal Rolling Mill Control Using Practical Advanced Methods,” Springer-Verlag, New York, 2011.
http://dx.doi.org/10.1007/978-0-85729-067-0
[7] Mikell P. Groover, “Fundamental of Modern Manufacturing,” John Wiley & Sons, Hoboken, 2007.
[8] M. Kutz, “Mechanical Engineers’ Handbook Manufacturingand Management,” John Wiley & Sons, Hoboken, 2006.
[9] B. Xu and P. Qian “Application of Adaptive Strategy Based on Model Prediction for the Stripe Thickness in Cold Rolling,” IEEE International Conference on Mechanic Automation and Control Engineering, 2010, pp. 3278-3281.
[10] S.-B. Tan and J.-C. Liu, “Research on Mill Modulus Control of Strip Rolling AGC Systems,” IEEE International Conference on Control and Automation, Guang zhou, May 30-June 1 2007, pp. 497-500.
[11] A. Kugi, W. Haas, K. Schlacher, K.Aistleitner, H. M. Frank and G. W. Rigler, “Active Compensation of Roll Eccentricity in Rolling Mills,” IEEE Transactions on Industry Applications, Vol. 36, No. 2, 2000, pp. 625-632.
http://dx.doi.org/10.1109/28.833781
[12] J. Pittner and M. A. Simaan, “An Optimal Control Method for Improvement in Tandem Cold Metal Rolling,” IEEE Transactions on Industry Applications Annual Meeting, 2007, pp. 382-389.
[13] C.-T. Li and C. S. G. Lee, “Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligen,” Prentice-Hall, New Jersey, 1996.
[14] A. Abraham, “Adaptation of Fuzzy Inference System Using Neural Learning,” Springer-Verlag, Berlin, Heidelberg, 2005.
[15] M. S. Mostafa, M. A. El-Bardini, S. M. Sharaf and M. M. Sharaf, “Fuzzy Neural Networks for Identification and Control of DC Drive Systems,” IEEE International Conference on Control Applications, Vol. 1, 2004, pp. 598603.
[16] A. ThamerRadhi, “Power System Protection Using Fuzzy Neural Petri Net,” Ph.D. Thesis, Basrah University, Iraq, 2012.
[17] S. A. H. A. Kareem, “Fuzzy Neural and Fuzzy Neural Petri Nets Control for Robot Arm,” MSc. Thesis, Basrah University, Iraq, 2010.
[18] Y. I. Al-Mashhadany, “Modeling and Simulation of Adaptive Neuro-Fuzzy Controller for Chopper-Fed DC Motor Drive,” IEEE Applied Power Electronics Colloquium (IAPEC), 2011, pp. 110-115.
[19] M. Dong, C. Liu and G. Y. Li, “Robust Fault Diagnosis Based on Nonlinear Model of Hydraulic Gauge Control System on Rolling Mill,” IEEE Transactions on Control Systems Technology, Vol. 18, No. 2, 2010, pp. 510-515.
http://dx.doi.org/10.1109/TCST.2009.2019750
[20] L. E. Zarate and F. R. Bittencout, “Representation and Control of the Cold Rolling Process through Artificial Neural Networks via Sensitivity Factors,” Elsevier Journal of Materials Processing Technology, 2007, pp. 344362.

  
comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.