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Combined Fuzzy Controllers with Embedded Model for Automation of Complex Industrial Plants

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Recent Developments and the New Direction in Soft-Computing Foundations and Applications

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 393))

Abstract

This paper devoted to design of the combined fuzzy controllers (CFC) with built-in model for the automatic control systems (ACS) of the complex industrial plants (CIP). The proposed CFC is designed for the ACS of the reactor temperature of the specialized pyrolysis plant (SPP) and tested in comparison with other controllers to demonstrate its advantages. The analysis of the computer simulation results confirms the high efficiency of the CFC with the proposed structure.

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References

  1. B.R. Mehta, Y.J. Reddy, Chapter 7—SCADA systems, in Industrial Process Automation Systems (2015), pp. 237–300

    Google Scholar 

  2. Y. Kondratenko, O. Gerasin, A. Topalov, A simulation model for robot’s slip displacement sensors. Int. J. Comput. 15(4), 224–236 (2016)

    Article  Google Scholar 

  3. Y.P. Kondratenko, O.V. Kozlov, O.V. Korobko, A.M. Topalov, Internet of things approach for automation of the complex industrial systems, in Proceedings of the 13th International Conference ICTERI’2017, CEUR-WS, vol. 1844, Kyiv, Ukraine, ed. by V. Ermolayev, et al. (2017), pp. 3–18

    Google Scholar 

  4. M. Pasieka, N. Grzesik, K. Kuźma, Simulation modeling of fuzzy logic controller for aircraft engines. Int. J. Comput. 16(1), 27–33 (2017)

    Article  Google Scholar 

  5. L.A. Zadeh, Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Article  Google Scholar 

  6. M. Jamshidi, N. Vadiee, T.J. Ross (eds.), Fuzzy Logic and Control: Software and Hardware Application. Prentice Hall Series on Environmental and Intelligent Manufacturing Systems, vol. 2 (Prentice Hall, Englewood Cliffs, NJ, 1993)

    Google Scholar 

  7. M.T. Hayajneh, S.M. Radaideh, I.A. Smadi, Fuzzy logic controller for overhead cranes. Eng. Comput. 23(1), 84–98 (2006)

    Article  Google Scholar 

  8. W.A. Lodwick, J. Kacprzhyk (eds.), Fuzzy Optimization. STUDFUZ, vol. 254 (Springer, Berlin, Heidelberg, 2010)

    Google Scholar 

  9. L.A. Zadeh, A.M. Abbasov, R.R. Yager, S.N. Shahbazova, M.Z. Reformat (eds.), Recent Developments and New Directions in Soft Computing. STUDFUZ, vol. 317 (Springer, Cham, 2014)

    Google Scholar 

  10. Y.P. Kondratenko, L.P. Klymenko, E.Y.M. Al Zu’bi, Structural optimization of fuzzy systems’ rules base and aggregation models. Kybernetes 42(5), 831–843 (2013)

    Article  Google Scholar 

  11. Y.P. Kondratenko, T.A. Altameem, E.Y.M. Al Zubi, The optimization of digital controllers for fuzzy systems design. Adv. Model. Anal. AMSE Period. Ser. A 47, 19–29 (2010)

    Google Scholar 

  12. L.A. Zadeh, The role of fuzzy logic in modeling, identification and control, modeling identification and control. Model. Identif. Control 15(3), 191–203 (1994)

    Article  MathSciNet  Google Scholar 

  13. J. Kacprzyk, R.R. Yager, S. Zadrożny, A fuzzy logic based approach to linguistic summaries of databases. Int. J. Appl. Math. Comput. Sci. 10(4), 813–834 (2000)

    MATH  Google Scholar 

  14. A. Piegat, Fuzzy Modeling and Control (Physica-Verlag, Heidelberg, New York, 2001)

    Book  Google Scholar 

  15. Y.P. Kondratenko, E.Y.M. Al Zubi, The optimization approach for increasing efficiency of digital fuzzy controllers, in Annals of DAAAM for 2009 & Proceeding of the 20th International DAAAM Symposium on Intelligent Manufacturing and Automation (2009), pp. 1589–1591

    Google Scholar 

  16. Y. Kondratenko, D. Simon, Structural and parametric optimization of fuzzy control and decision making systems, in Recent Developments and the New Direction in Soft-Computing Foundations and Applications. Selected Papers from the 6th World Conference on Soft Computing, May 22–25, 2016, Berkeley, USA. Series: Studies in Fuzziness and Soft Computing, vol. 361, ed. by L. Zadeh, R.R. Yager, S.N. Shahbazova, M.Z. Reformat, V. Kreinovich (Springer International Publishing, Cham, 2018) pp. 273–289. https://doi.org/10.1007/978-3-319-75408-6_22

  17. Y. Kondratenko, V. Korobko, O. Korobko, G. Kondratenko, O. Kozlov, Green-IT approach to design and optimization of thermoacoustic waste heat utilization plant based on soft computing, in Green IT Engineering: Components, Networks and Systems Implementation. Studies in Systems, Decision and Control, vol. 105, ed. by V. Kharchenko, Y. Kondratenko, J. Kacprzyk (Springer, Cham, 2017), pp. 287–311

    Google Scholar 

  18. L. Wang, T.J. Kazmierski, VHDL-AMS based genetic optimisation of fuzzy logic controllers. Int. J. Comput. Math. Electric. Electron. Eng. 26(2), 447–460 (2007)

    Article  Google Scholar 

  19. D. Simon, Design and rule base reduction of a fuzzy filter for the estimation of motor currents. Int. J. Approx. Reason. 25, 145–167 (2000)

    Article  Google Scholar 

  20. R. Alcalá, J. Alcalá-Fdez, M.J. Gacto, F. Herrera, Rule base reduction and genetic tuning of fuzzy systems based on the linguistic 3-tuples representation. Soft. Comput. 11(5), 401–419 (2007)

    Article  Google Scholar 

  21. W. Pedrycz, K. Li, M. Reformat, Evolutionary reduction of fuzzy rule-based models, in Fifty Years of Fuzzy Logic and its Applications, STUDFUZ, vol. 326 (Springer, Cham, 2015), pp. 459–481

    Google Scholar 

  22. D. Simon, Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence (Wiley, 2013)

    Google Scholar 

  23. Q. Suna, R. Li, P. Zhang, Stable and optimal adaptive fuzzy control of complex systems using fuzzy dynamic model. J. Fuzzy Sets Syst. 133, 1–17 (2003)

    Article  MathSciNet  Google Scholar 

  24. R.R. Yager, D.P. Filev, Unified structure and parameter identification of fuzzy models. Syst. Man Cybern. 23(4) (1993)

    Google Scholar 

  25. R. Hampel, M. Wagenknecht, N. Chaker, Fuzzy Control: Theory and Practice (Physika-Verlag, Heidelberg, New York, 2000)

    Book  Google Scholar 

  26. S.K. Oh, W. Pedrycz, The design of hybrid fuzzy controllers based on genetic algorithms and estimation techniques. J. Kybernetes 31(6), 909–917 (2002)

    Article  Google Scholar 

  27. K. Tanaka, H.O. Wang, Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach (Wiley, New York, USA, 2001)

    Book  Google Scholar 

  28. R.R. Yager, D.P. Filev, Essentials of Fuzzy Modeling and Control (Wiley, New York, NY, 1994)

    Google Scholar 

  29. T. Takagi, M. Sugeno, Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15(1)

    Google Scholar 

  30. Y.P. Kondratenko, O.V. Kozlov, O.V. Korobko, A.M. Topalov, Synthesis and optimization of fuzzy control system for floating dock’s docking operations, in Fuzzy Control Systems: Design, Analysis and Performance Evaluation, ed. by W. Santos (Nova Science Publishers, New York City, USA, 2017), pp. 141–215

    Google Scholar 

  31. I. Skrjanc, Design of fuzzy model-based predictive control for a continuous stirred-tank reactor, in 12th Zittau Fuzzy Colloquium, Proceedings of East-West Fuzzy Colloquium, Zittau, Hochschule Zittau/Goerlitz, Germany (2005), pp. 126–139

    Google Scholar 

  32. C. Chen, Y.M. Chen, Self-organizing fuzzy logic controller design. J. Comput. Ind. 22(3), 249–261 (1993)

    Article  Google Scholar 

  33. A.A. Tunik, M.M. Komnatska, On structures of combined UAV flight control systems with elements of fuzzy logics. J. Electron. Control Syst. 3(41), 20–28 (2014)

    Google Scholar 

  34. Y.P. Kondratenko, O.V. Kozlov, G.V. Kondratenko, I.P. Atamanyuk, Mathematical model and parametrical identification of ecopyrogenesis plant based on soft computing techniques, in Complex Systems: Solutions and Challenges in Economics, Management and Engineering, Studies in Systems, Decision and Control, vol. 125, ed. by C. Berger-Vachon, et al. (Springer, Berlin, Heidelberg, 2018), pp. 201–233

    Google Scholar 

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Acknowledgements

Many thanks to Fulbright Scholar Program, Institute of International Education and Ukrainian Fulbright Circle for the support of this research and for Prof. Kondratenko’s possibility to conduct research at Cleveland State University.

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Authors and Affiliations

  1. Department of Intelligent Information Systems, Petro Mohyla Black Sea National University, Mykolaiv, 54003, Ukraine

    Yuriy P. Kondratenko

  2. Department of Computerized Control Systems, Admiral Makarov National University of Shipbuilding, Mykolaiv, 54025, Ukraine

    Oleksiy V. Kozlov

Authors
  1. Yuriy P. Kondratenko
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  2. Oleksiy V. Kozlov
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Corresponding author

Correspondence to Yuriy P. Kondratenko .

Editor information

Editors and Affiliations

  1. Department of Information Technology and Programming, Azerbaijan Technical University, Baku, Azerbaijan

    Shahnaz N. Shahbazova

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  3. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  4. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Vladik Kreinovich

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Kondratenko, Y.P., Kozlov, O.V. (2021). Combined Fuzzy Controllers with Embedded Model for Automation of Complex Industrial Plants. In: Shahbazova, S.N., Kacprzyk, J., Balas, V.E., Kreinovich, V. (eds) Recent Developments and the New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol 393. Springer, Cham. https://doi.org/10.1007/978-3-030-47124-8_18

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