Skip to main content
SpringerLink
Log in

The Fuzzy Rule Base Automatic Optimization Method of Intelligent Controllers for Technical Objects Using Fuzzy Clustering

  • Conference paper
  • First Online:
Creativity in Intelligent Technologies and Data Science (CIT&DS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1084))

Included in the following conference series:

Abstract

The goal of this work is to develop a fuzzy rule base optimization method of intelligent controllers for technical objects using fuzzy clustering. To achieve the goal of the study, a hybrid model is developed in which the control of a technical object is implemented using the PID-classical and PID-FUZZY controllers with the generated structure of the Sugeno-type fuzzy inference system and the developed model of the adaptive neuro-fuzzy inference system, which allows to form a base of fuzzy rules, which is independent of an expert knowledge in the subject area, a method for optimization the fuzzy controller rule base based on clustering methods is proposed, allows you to reduce the number of fuzzy inference rules and increase the control system speed of the technical object. A hybrid model was simulated before and after fuzzy clustering, which proves the high efficiency of the proposed method for optimization the fuzzy controller rule base by reducing the number of fuzzy inference rules and the number of membership functions required to describe linguistic variables.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Demidova, G.L., Lukichev, D.V.: Controllers based on fuzzy logic in control systems for technical objects, 81 p. University of ITMO, Saint-Petersburg (2017). (in Russian)

    Google Scholar 

  2. Vasil’ev, V.I., Il’jasov, B.G.: Intelligent control systems. Theory and practice, Moscow, 392 p. (2009). (in Russian)

    Google Scholar 

  3. Gostev, V.I.: Designing fuzzy controllers for automatic control systems, 416 p. BHV-Peterburg, Saint-Petersburg (2011) (in Russian)

    Google Scholar 

  4. Demidova, G.L., Kuzin, A.Yu., Lukichev, D.V.: Application features of fuzzy controllers on example of DC motor speed control. Sci. Tech. J. Inf. Technol. Mech. Opt. 16(5), 872–878 (2016). (in Russian)

    Google Scholar 

  5. Leonenkov, A.V.: Fuzzy modeling with MATLAB and FuzzyTECH, 736 p. BHV-Petersburg, Saint-Petersburg (2005). (in Russian)

    Google Scholar 

  6. Shtovba, S.D.: Design of fuzzy systems by means of MATLAB, 288 p. Hot line-Telecom (2007). (in Russian)

    Google Scholar 

  7. Kolesnikov, A.V.: Hybrid Intelligent Systems: Theory and Technology of Development, 600 p. Publishing House SPbSTU, Saint-Petersburg (2001). (in Russian)

    Google Scholar 

  8. Demenkov, N.P.: Fuzzy control in technical systems: a training manual, 200 p. Publishing House of MSTU of N.E. Bauman (2005). (in Russian)

    Google Scholar 

  9. Manenti, F., et al.: Fuzzy adaptive con-trol system of a non-stationary plant with closed-loop passive identifier. Resour.-Efficient Technol. 1, 10–18 (2015)

    Article  Google Scholar 

  10. Kudinov, Y.I., Kolesnikov, V.A., Pashchenko, F., Pashchenko, A., Papic, L.: Optimization of fuzzy PID controller’s parameters. In: XIIth International Symposium «Intelligent Systems», INTELS 2016, Moscow, Russia, 5–7 October 2016 (2017). Procedia Comput. Sci. 103, 618–622

    Google Scholar 

  11. Kharola, A., Patil, P., Raiwani, S., Rajput, D.: A comparison study for control and stabilisation of inverted pendulum on inclined surface (IPIS) using PID and fuzzy controllers. Perspect. Sci. 8, 187–190 (2016)

    Article  Google Scholar 

  12. Karli, A., Omurlu, V.E., Buyuksahin, U., Artar, R., Ortak, E.: Self tuning fuzzy PD application on TI TMS320F 28335 for an experimental stationary quadrotor

    Google Scholar 

  13. Beirami, H., Shabestari, A.Z., Zerafat, M.M.: Optimal PID plus fuzzy controller design for a PEM fuel cell air feed system using the self-adaptive differential evolution algorithm. Int. J. Hydrogen Energy 40(30), 9422–9434 (2015)

    Article  Google Scholar 

  14. Savran, A.: A multivariable predictive fuzzy PID control system. Appl. Soft Comput. 13, 2658–2667 (2013)

    Article  Google Scholar 

  15. Liem, D.T., Truong, D.Q., Ahn, K.K.: A torque estimator using online tun-ing grey fuzzy PID for applications to torque-sensorless control of DC motors. Mechatronics 26, 45–63 (2015)

    Article  Google Scholar 

  16. Savran, A., Kahraman, G.: A fuzzy model based adaptive PID controller design for nonlinear and uncertain processes. ISA Trans. 53, 280–288 (2014)

    Article  Google Scholar 

  17. Jahedi, G., Ardehali, M.M.: Genetic algorithm-based fuzzy-PID control methodologies for enhancement of energy efficiency of a dynamic energy system. Energy Convers. Manag. 52, 725–732 (2011)

    Article  Google Scholar 

  18. de la Hermosa Gonzalez-Carrato, R.R.: Wind farm monitoring using Mahalanobis distance and fuzzy clustering. Renew. Energy 123, 526–540 (2018)

    Google Scholar 

  19. Jang, J.S.R.: ANFIS: adaptive network based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–684 (1993)

    Article  Google Scholar 

  20. Chaghari, A., Feizi-Derakhshi, M.-R., Balafar, M.-A.: Fuzzy clustering based on Forest optimization algorithm. J. King Saud Univ. – Comput. Inf. Sci. 30, 25–32 (2018)

    Google Scholar 

  21. Ren, Q., Bigras, P.: Discrete-time parallel robot motion control using adaptive neuro-fuzzy inference system based on improved subtractive clustering. In: 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016; Vancouver; Canada; 24 July 2016–29 July 2016 (2016). https://doi.org/10.1109/fuzz-ieee.2016.7737797

  22. Polkovnikova, N.A., Kureichik, V.M.: Neural network technologies, fuzzy clustering and genetic algorithms in the VLSI expert system. In: Proceedings of the Southern Federal University (2014). (in Russian). Eng. Sci. 7(156), 7–15

    Google Scholar 

  23. Salgado, P., Igrejas, G.: Probabilistic clustering algorithms for fuzzy rules decomposition. In: 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. № 04CH37583), pp. 115–120 (2004)

    Google Scholar 

  24. dos Santos Coelho, L., Pessoa, M.W., Mariani, V.C., Reynoso-Meza, G.: Fuzzy inference system approach using clustering and differential evolution optimization applied to identification of a twin rotor system. IFAC PapersOnLine 50(1), 13102–13107 (2017)

    Article  Google Scholar 

  25. Ignatyev, V.V., Kureychik, V.M., Spiridonov, O.B., Ignatyeva, A.S.: The method of hybrid control based on the adaptive system of neuro-fuzzy inference. Bulletin of SFedU. Technical science, № 9(194), 2302 p., pp. 124–1320. ITA SFedU publishing house, Taganrog (2017). ISSN 1999-9429. (in Russian)

    Google Scholar 

  26. Ignatyev, V.V., Kovalev, A.V., Spiridonov, O.B., Ignatyeva, A.S., Bozhich, V.I., Boldyreff, A.S.: Model of adaptive system of neuro-fuzzy inference based on PI- and PI-fuzzy-controllers. In: Proceedings of the SPIE 10799, Emerging Imaging and Sensing Technologies for Security and Defence III; and Unmanned Sensors, Systems, and Countermeasures, SPIE Security + Defence, 2018, Berlin, Germany, vol. 10799 107990Y-1, 4 October 2018. https://doi.org/10.1117/12.2513302

  27. Ignatyev, V.V., Spiridonov, O.B.: Hybrid algorithm for formation the fuzzy controller rule base. In: Bulletin of SFedU. Technical science. Thematic issue “Radio-electronic and infocommunication technologies, systems and networks”, № 11(172), pp. 177–186. ITA SFedU Publishing House, Taganrog (2015). (in Russian)

    Google Scholar 

  28. Ignatyev, V.V., Spiridonov, O.B., Soloviev, V.V., Beloglazov, D.A., Ignatyeva, A.S.: Hybrid control simulation of a technical object using the system of Sugeno type fuzzy inference. In: Proceedings of the Congress on Intelligent Systems and Information Technologies, IS&IT 2018, Scientific publication in 3 volumes, 418 p., pp. 29–35. Stupin A.S. publishing House, Taganrog (2018). ISBN 978-5-6041321-4-2, ISBN 978-5-6041321-6-6 (T. 2). (in Russian)

    Google Scholar 

  29. Ignatyev, V.V., Ignatyeva, A.S., Spiridonov, O.B., Shapovalov, I.O., Soloviev, V.V.: Optimization of fuzzy controller rule base on the basis of clustering methods. In: Proceedings of the Congress on Intelligent Systems and Information Technologies, IS&IT 2018, Scientific publication in 3 volumes, 418 p., pp. 35–44. Stupin A.S. Publishing House, Taganrog (2018). ISBN 978-5-6041321-4-2, ISBN 978-5-6041321-6-6 (T. 2). (in Russian)

    Google Scholar 

  30. Kruglov, V.V., Dli, M.I., Gplubov, R.Yu.: Fuzzy logic and artificial neural networks, 224 p. (2004). (in Russian)

    Google Scholar 

Download references

Acknowledgments

This work was supported by Grant of the Russian Foundation for Basic Research (№ 18-38-00711) at the Southern Federal University, State task of subordinate educational organizations for the implementation of the project on the theme «Development, research and producing of an automated control system for the training of microwave devices». Project PP0708-11/2017-09 (Task № 8.3795.2017/PP), Grant of the Russian Foundation for Basic Research (№ 18-07-000-50).

Author information

Authors and Affiliations

  1. Southern Federal University, Bolshaya Sadovaya Str., 105/42, 344006, Rostov-on-Don, Russia

    Vladimir Ignatyev, Viktor Soloviev, Denis Beloglazov, Viktor Kureychik, Kovalev Andrey & Alexandra Ignatyeva

Authors
  1. Vladimir Ignatyev
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Viktor Soloviev
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Denis Beloglazov
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Viktor Kureychik
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kovalev Andrey
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Alexandra Ignatyeva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vladimir Ignatyev .

Editor information

Editors and Affiliations

  1. CAD & Research Department, Volgograd State Technical University, Volgograd, Russia

    Alla G. Kravets

  2. Electrical and Computer Engineering, University of Patras, Rion, Patras, Greece

    Peter P. Groumpos

  3. Volgograd State Technical University, Volgograd, Russia

    Maxim Shcherbakov

  4. Volgograd State Technical University, Volgograd, Russia

    Marina Kultsova

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ignatyev, V., Soloviev, V., Beloglazov, D., Kureychik, V., Andrey, K., Ignatyeva, A. (2019). The Fuzzy Rule Base Automatic Optimization Method of Intelligent Controllers for Technical Objects Using Fuzzy Clustering. In: Kravets, A., Groumpos, P., Shcherbakov, M., Kultsova, M. (eds) Creativity in Intelligent Technologies and Data Science. CIT&DS 2019. Communications in Computer and Information Science, vol 1084. Springer, Cham. https://doi.org/10.1007/978-3-030-29750-3_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-29750-3_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29749-7

  • Online ISBN: 978-3-030-29750-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation