Fuzzy Logic Based Dynamic Performance Investigation of Moisture Control in Paper Industry

Article Preview

Abstract:

Proportional – Integral – Derivatives control scheme is used to provide an efficient and quiet easier in control engineering applications. Most of the Conventional PID tuning methods are used in manually which is difficult and time consuming. Soft Computing Technique is used to overcome this problem by using Fuzzy logic technique. In this research focuses dynamic performance specification is such as rise time, settling time and peak over shoot. This paper proposes to compare conventional and non-conventional techniques and the result of this comparison, The non-conventional technique (Fuzzy logic) has optimal dynamic performance. The plant model is represented by the transfer function, is obtained by the system identification tool box.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

254-259

Citation:

Online since:

June 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Johan Akesson, Ola Sl¨atteke, Modeling, Calibration and Control of a Paper Machine Dryer Section, Modelica 2006, September 4–5.

Google Scholar

[2] Xuetingwang, shengshuocao, modelling paper drying with ComsolMultiphysicsModeling Tool" Bachelor, s Thesis, (2013).

Google Scholar

[3] Magnus Karlsson, Ola Slätteke, Tore Hägglund, and StigStenström, Feedforward Control in the Paper Machine Drying Section, Proceedings of the 2006 American Control Conference, June 14-16, (2006).

DOI: 10.1109/acc.2006.1656506

Google Scholar

[4] Ajit K Ghosh, fundamentals of paper drying-theory and application from industrial perspective, Evaporation, Condensation and Heat Transfer.

DOI: 10.5772/21594

Google Scholar

[5] Yeong-Koo Yeo†, Ki-Seok Hwang, Sung Chul Yi and Hong Kang, Modeling of the Drying Process in Paper Plants, Korean J. Chem. Eng., pp-761-766, (2004).

DOI: 10.1007/bf02705517

Google Scholar

[6] Chang Hoe Heo, Hyunjun Cho, and Yeong-Koo Yeo, Dynamic modeling of paper drying processes, Korean J. Chem. Eng., pp- 1651-1657, (2011).

DOI: 10.1007/s11814-011-0046-0

Google Scholar

[7] Anita Zvolinschi, EivindJohannessen, Signe Kjelstrup, The second-lawoptimal operation of a paper drying machine, Chemical Engineering Science, pp-3653 – 3662, (2006).

DOI: 10.1016/j.ces.2005.12.030

Google Scholar

[8] T. Lu,S.Q. Shen, Numerical and experimental investigation of paper drying: Heat and mass transfer with phase change in porous media, Applied Thermal Engineering, pp-1248–1258, (2007).

DOI: 10.1016/j.applthermaleng.2006.11.005

Google Scholar

[9] Timothy F. Murphy, Stephen Yurkovich, and Shih-Chin Chen, Intelligent control for paper machine moisture control, proceeding of the 1996 IEE international conference on control application Dearborn, September, pp: 15-18, (1996).

DOI: 10.1109/cca.1996.558974

Google Scholar

[10] ErandaHarinath, George K. I. Mann, Design of tuning of standard additive model based fuzzy PID controllers for multivariable process syestems, IEEE transactions on systems, man , and cybernetics, vol. 38, No. 3, june (2008).

DOI: 10.1109/tsmcb.2008.919232

Google Scholar

[11] Keyu Li, PID Tuning for Optimal Closed-loop performance with specified gain and phase margins, IEEE transactions on control systems technology, vol. 21, No. 3, may2013.

DOI: 10.1109/tcst.2012.2198479

Google Scholar

[12] K. S. Tang, Kim Fung Man, Guanrong Chen, Fellow, An Optimal Fuzzy PID Controller, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, vol. 48, No. 4, August 20.

DOI: 10.1109/41.937407

Google Scholar

[13] Wen Tan, unified tuning of PID load frequency controller for power systems via IMC, IEEE TRANSACTIONS ON POWER SYSTEMS, Vol. 25, No. 1, Feb (2010).

DOI: 10.1109/tpwrs.2009.2036463

Google Scholar

[14] Kiyong Kim, Member, PraneshRao, and Jeffrey A. Burnworth, Self-Tuning of the PID Controller for a digital Excitation Control system, IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, Vol. 46, No. 4, JULY/August2010.

DOI: 10.1109/tia.2010.2049631

Google Scholar

[15] Vicente Milanés, Jorge Villagrá, Jorge Godoy, and Carlos González, Comparing Fuzzy and Intelligent PI Controllers in Stop-and-Go Manoeuvres, IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, Vol. 20, No. 3, May (2012).

DOI: 10.1109/tcst.2011.2135859

Google Scholar

[16] Zaojun Fang, De Xu and Min Tan, A Vision-Based Self-tunning fuzzy controller for Fillet Weld Seam Tracking, IEEE/ASME TRANSACTIONS ON MECHATRONICS, Vol. 16, No. 3, June (2011).

DOI: 10.1109/tmech.2010.2045766

Google Scholar

[17] Xinyu Du, Student Member, and Hao Ying, Derivation and Analysis of the Analytical Structures of the interval type-2 fuzzy-PI and PD controller, IEEE TRANSACTIONS ON FUZZY SYSTEMS, Vol. 18, No. 4, Augu.

DOI: 10.1109/tfuzz.2010.2049022

Google Scholar