Advantages of Fuzzy Control Application in Fast and Sensitive Technological Processes

Authors: Radim Farana, Bogdan Walek, Michal Janosek, Jaroslav Zacek

Abstract:

This paper presents the advantages of fuzzy control use in technological processes control. The paper presents a real application of the Linguistic Fuzzy-Logic Control, developed at the University of Ostrava for the control of physical models in the Intelligent Systems Laboratory. The paper presents an example of a sensitive non-linear model, such as a magnetic levitation model and obtained results which show how modern information technologies can help to solve actual technical problems. A special method based on the LFLC controller with partial components is presented in this paper followed by the method of automatic context change, which is very helpful to achieve more accurate control results. The main advantage of the used system is its robustness in changing conditions demonstrated by comparing with conventional PID controller. This technology and real models are also used as a background for problem-oriented teaching, realized at the department for master students and their collaborative as well as individual final projects.

Keywords: Control, fuzzy logic, sensitive system, technological proves.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1109651

References:

[1] L. A. Zadeh, “Fuzzy sets,” Information & Control, vol. 8, 1965, pp. 338-353.
[2] L. A. Zadeh, L. A. and J. Kacprzyk, “Fuzzy Logic for the Management of Uncertainty,” J. Wiley & Sons, New York 1992.
[3] E. Mamdani and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic control,” Int. J. of Man-Machine Studies, Vol. 7, 1975, pp. 1–13.
[4] T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Trans. on Systems, Man, and Cybern, Vol. 15, 1985, pp. 116–132.
[5] V. Novák, “Linguistically Oriented Fuzzy Logic Control and Its Design,” International Journal of Approximate Reasoning, vol. 12, 1995, pp. 263-277.
[6] V. Novák and I. Perfilieva, “Evaluating Linguistic Expressions and Functional Fuzzy Theories in Fuzzy Logic,” Computing with Words in Information/Intelligent Systems 1, L. A. Zadeh a J. Kacpryk (eds.), Springer-Verlag, Heidelberg, 1999, pp. 383-406.
[7] V. Novák, “Genuine Linguistic Fuzzy Logic Control: Powerful and Successful Control Method,” Computational Intelligence for Knowledge-Based Systems Design, Hüllermeier, E. and Kruse, R. and Hoffmann, F. (eds.), Springer, Berlin, 2010, pp. 634 -644.
[8] HUMUSOFT. CE 150 “Magnetic Levitation Model“ (online), 2015 (cit 2015-08-10). Available on web: http://www.humusoft.cz/produkty/ models/ce150/
[9] R. Farana & B. Walek & M. Janošek and J. Žáček, ”Fuzzy-Logic Control in Fast Technological Processes”, in Proceedings of the 2014 15th International Carpathian Control Conference (ICCC), Velke Karlovice, Czech Republic, VŠB-TU Ostrava, 28. – 30. 6. 2014, pp. 105 – 108, ISBN 978-1-4799-3527-7 (CD), 978-147993528-4 (Scopus), IEEE Catalogue Number: CFP1442L-CDR. DOI: 10.1109/CarpathianCC.2014.6843578
[10] L. Cedro and D. Janecki, “Determining of Signal Derivatives in Identification Problems - FIR Differential Filters,” Acta Montanistica Slovaca, Volume 16, Issue 1, 2011, pp. 47 – 54, ISSN 1335-1788.
[11] V. Novák, “Fuzzy modeling principles (in Czech),” 1. ed. BEN-Technická literatura, Praha, 2000, 175 pp. ISBN 80-7300-009-1.
[12] R. Farana & B. Walek & M. Janošek and J. Žáček, “Application of Linguistic Fuzzy-Logic Control in Fast and Sensitive Technological Processes”, in 38th International Conference on Telecommunications and Signal Processing“. Prague, July 9-11, 2015, pp. 266-270. ISBN 978-1-4799-8497-8, ISSN 1805-5435, IEEE Catalog Number CFP1588P-USB.
[13] J. E. Takosoglu, P. A. Laski and S. Blasiak, “A fuzzy logic controller for the positioning control of an electro-pneumatic servo-drive,” Journal of Systems and Control Engineering, Volume 226, Issue 10, November 2012, pp. 1335-1343.
[14] W. F. Godoy, I. N. Da Silva, A. Goedtel and R.H.C. Palácios, ”Fuzzy logic applied at industrial roasters in the temperature control,” in 11th IFAC Workshop on Intelligent Manufacturing Systems (IMS 2013), Sao Paulo, Brazil, 2013, pp. 450-455.
[15] J. Velagic and N. Osmic, “Fuzzy-Genetic Identification and Control Structures for Nonlinear Helicopter Model,” Intelligent Automation & Soft Computing, Volume 19, Issue 1, pp. 51-68, ISSN 1079-8587. DOI: 10.1080/10798587.2013.771454.