Optimal fuzzy controller in the carbonization control system at the soda production

Article Sidebar

PDF (Українська)
DOI: https://doi.org/10.20998/2522-9052.2019.2.03
Keywords:
carbonization, fuzzy UID controller, membership functions, Fuzzy setup parameters

Main Article Content

Eduard German
National Technical University “Kharkiv Polytechnic Institute”, Kharkiv
https://orcid.org/0000-0002-8221-033X
Oleksii Shutinskyi
National Technical University “Kharkiv Polytechnic Institute”, Kharkiv
https://orcid.org/0000-0002-4288-9309
Igor Lysachenko
National Technical University “Kharkiv Polytechnic Institute”, Kharkiv
https://orcid.org/0000-0002-3723-8587
Svetlana Demenkova
National Technical University “Kharkiv Polytechnic Institute”, Kharkiv
https://orcid.org/0000-0001-6596-6605

Abstract

The subject of research in this article is the procedure for the synthesis of a fuzzy controller in the carbonation control system in the production of soda ash. The aim of the work is the development of optimal fuzzy controller, the use of which in the control system will condition the optimal result according to the established criteria. Task: based on the existing approaches to the synthesis of FLC, to develop a procedure for the synthesis of the optimal fuzzy PID controller corresponding to the control problems. The procedure for construct of controller depends both on the structure of the controller and on the parameters of fuzzy tuning, such as quantity of the membership function, their appearance, and the parameters that determine the membership function. As a result of the simulation of the carbonization section control system, the dependence of the final result on fuzzy tuning parameters was shown. Conclusions: on the basis of the proposed synthesis methods, the structures of optimal FPIDC with different sets of fuzzy tuning parameters were developed. It is shown that any optimal fuzzy PID controller gives better performance than the classic controller, and the best of them are the controller in which the fuzzy tuning block has Gaussian membership functions.

Article Details

How to Cite
German, E., Shutinskyi, O., Lysachenko, I., & Demenkova, S. (2019). Optimal fuzzy controller in the carbonization control system at the soda production. Advanced Information Systems, 3(2), 14–21. https://doi.org/10.20998/2522-9052.2019.2.03
Issue
Vol. 3 No. 2 (2019): Advanced Information Systems
Section
Adaptive control methods
Author Biographies

Eduard German, National Technical University “Kharkiv Polytechnic Institute”, Kharkiv

PhDof tech. sci., Associate Professor of Automation of the Technological Systems and Ecological Monitoring Department

Oleksii Shutinskyi, National Technical University “Kharkiv Polytechnic Institute”, Kharkiv

PhDof tech. sci., Associate Professor of Automation of the Technological Systems and Ecological Monitoring Department

Igor Lysachenko, National Technical University “Kharkiv Polytechnic Institute”, Kharkiv

PhDof tech. sci., Associate Professor of Automation of the Technological Systems and Ecological Monitoring Department

Svetlana Demenkova, National Technical University “Kharkiv Polytechnic Institute”, Kharkiv

Assistant of Automation of the Technological Systems and Ecological Monitoring Department

References

Miroshnik M.A., German E.E., Kotukh V.G., Zagumennaya E.V. (2015), “Design of artificial intelligence systems with using fuzzy logic”, Radio engineering: All-Ukr. interdep. sci.-tech. collection, Vol. 182, pp. 42-50.

German E.E. (2008), “Current state and development prospects of fuzzy control systems”, Herald of National Technical Uni-versity “Kharkiv Polytechnic Institute”, Vol. 57, pp. 37–44.

Lee C.C. (1990), “Fuzzy Logic in control systems: Fuzzy Logic Controller – Part I”, IEEE Transactions on Systems, Man and Cybernetics, Vol. 20, № 2, pp. 404–418.

Piegat A. (2009), Fuzzy Modelling and Control, BINOM, Knowledge Laboratory, 798 p.

Mamdani E.H. (1974), “Application of fuzzy algorithms for the control of a dynamic plant”, Proceedings of the Institution of Electrical Engineers. Vol 121, № 12, pp. 1585–1588.

Takagi T., Sugeno M. (1985), “Fuzzy identification of systems and its application to modeling and control”, IEEE Transactions on Systems, Man and Cybernetics. Vol 15, № 1, pp. 116-132.

Methods of robust, neuro-fuzzy and adaptive control: Textbook. ed..by Yegupov N. (2002), Bauman MSTU, 744 p.

German E.E. (2010), “Synthesis of hybrid controllers to control of the growing process of large-sized single crystals”, Infor-mation-control systems on railway transport, Vol. 3, pp. 53–56.

Jantzen J. (1998), Design of fuzzy controllers. Technical University of Denmark: Dept. of Automation. Lecture notes,

available at: http://faculty.petra.ac.id/resmana/private/fuzzy/design.pdf.

Fuzzy sets in control and artificial intelligence models, ed. by Pospelov D. (1986), Moscow. Science. 312 p.

German E.E., Gapon A.I., Derbunovich L.V. (2005), “Design methods for fuzzy PID controllers”, Herald of National Tech-nical University “Kharkiv Polytechnic Institute”. Automation and instrument making, Vol. 17, pp. 15-21.

German E.E., Shutinsky O.G., Makovoz O.M. (2016), “The use of a fuzzy controller in a carbonization control system in soda production”, Herald of National Technical University “Kharkiv Polytechnic Institute”. Chemistry, chemical technology and ecology, Vol. 35, pp. 24–27.

German E.E., Zlotov Ye.V. (2018), “Mathematical modeling of the carbonization column in the production of soda ash”, Infor-mation technologies: science, techniques, technology, education, health: abstracts of the XXVI International Scientific and Prac-tical Conference MicroCAD, p.164.

Chen G. (1996), “Conventional and fuzzy PID controllers: An overview”, International Journal of Intelligent Control and Systems. Vol. 1. pp. 235–246.

German E.E., Derbunovich L.V., Beletsky S.V. (2007), “Optimization of fuzzy PID controllers parameters”, Herald of Nation-al Technical University “Kharkiv Polytechnic Institute”, Vol. 36, pp. 3-8.

Dorf R.C., Bishop R H. (2014), Modern control systems, Boston, Prentice Hall, 1082 p.