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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  1/2010 - 2

Adaptive Automatic Gauge Control of a Cold Strip Rolling Process

ROMAN, N. See more information about ROMAN, N. on SCOPUS See more information about ROMAN, N. on IEEExplore See more information about ROMAN, N. on Web of Science, CEANGA, E. See more information about  CEANGA, E. on SCOPUS See more information about  CEANGA, E. on SCOPUS See more information about CEANGA, E. on Web of Science, BIVOL, I. See more information about  BIVOL, I. on SCOPUS See more information about  BIVOL, I. on SCOPUS See more information about BIVOL, I. on Web of Science, CARAMAN, S. See more information about CARAMAN, S. on SCOPUS See more information about CARAMAN, S. on SCOPUS See more information about CARAMAN, S. on Web of Science
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (520 KB) | Citation | Downloads: 1,377 | Views: 2,055

Author keywords
automatic gauge control, adaptive control, inverse model, adaptive filtering, optimization

References keywords
control(20), strip(7), rolling(6), looper(6), mills(4), mill(4), isij(4), grimble(4), applications(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2010-02-27
Volume 10, Issue 1, Year 2010, On page(s): 7 - 17
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.01002
Web of Science Accession Number: 000275458900002
SCOPUS ID: 77954670610

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The paper tackles with thickness control structure of the cold rolled strips. This structure is based on the rolls position control of a reversible quarto rolling mill. The main feature of the system proposed in the paper consists in the compensation of the errors introduced by the deficient dynamics of the hydraulic servo-system used for the rolls positioning, by means of a dynamic compensator that approximates the inverse system of the servo-system. Because the servo-system is considered variant over time, an on-line identification of the servo-system and parameter adapting of the compensator are achieved. The results obtained by numerical simulation are presented together with the data taken from real process. These results illustrate the efficiency of the proposed solutions.

References | Cited By  «-- Click to see who has cited this paper

[1] Asada H., Kitamura A., Nishino S., and Konishi M., "Adaptive and robust control method with estimation of rolling characteristics for looper angle control at hot strip mill", ISIJ International, Vol. 43 (No. 3), pp. 358-365, 2003
[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 33]

[2] Asano K., Yamamoto K., Kawase T. and Nomura N., "Hot strip mill tension - looper control based on decentralization and coordination", Control Engineering Practice, Vol. 8, No. 3, pp. 337-344, 2000
[CrossRef] [Web of Science Times Cited 24] [SCOPUS Times Cited 38]

[3] Hearns G. and Grimble M. J., "Inferential control for rolling mills", IEEE Proc. Control Theory and its Applications, 147 (No. 6), pp. 673-679, 2000
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 15]

[4] Hearns G. and Grimble M. J., "Robust multivariable control for hot strip mills", ISIJ International, Vol. 40 (No. 10), pp. 995-1002, 2000
[CrossRef] [Web of Science Times Cited 24] [SCOPUS Times Cited 33]

[5] Imanari H., Morimatsu Y., Sekiguchi K., Ezure H., Matuoka R., Tokuda A. and Otobe H., "Looper H control for hot strip mill", IEEE Transactions on industry applications, 33 (3), pp. 790-796, 1997

[6] Janabi-Sharifi F., "A neuro-fuzzy system for looper tension control in rolling mills", Control engineering practice, Vol. 13, No.1, pp. 1-13, 2005
[CrossRef] [Web of Science Times Cited 34] [SCOPUS Times Cited 50]

[7] Choi J. I. S., Rossiter J. A. and Fleming P. J., "Looper and tension control in hot rolling mills: A survey", Journal of Process Control, Vol. 17, Issue 6, July 2007, pp. 509-521
[CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 47]

[8] Konishi M., Imajo S., Imai J., Nishi T., "Modeling of gain tuning operation for hot strip looper controller by recurrent neural network", Proceedings of the 2004 IEEE International Conference on Control Applications, 2004, pp. 890-895

[9] Bulut B., Ordys A. W., Grimble M. J., "Application of efficient nonlinear predictive control to a hot strip finishing mill", Proceedings of the 2002 IEEE International conference on Control Applications 2002, pp. 373-378

[10] Bulut B., Katebi M. R., Grimble M. J., "Predictive Control of Hot Rolling Processes", Proceedings of the American Control Conference, Chicago, Illinois, June 2000, pp. 2058-2062

[11] Rath G., "Model Based Thickness Control of the Cold Strip Rolling Process", Ph.D. Thesis, Leoben, April 2000

[12] Rabbah N., Bensassi B., "Control of a Multivariable Web Winding System", The Annals of Dunarea de Jos University of Galati, Fascicle III, 2007, ISSN 1221-454x, Electrotechnics, Electronics, Automatic Control and Informatics, pp. 107-113

References Weight

Web of Science® Citations for all references: 147 TCR
SCOPUS® Citations for all references: 216 TCR

Web of Science® Average Citations per reference: 12 ACR
SCOPUS® Average Citations per reference: 18 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2018-09-21 02:26 in 63 seconds.

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania

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