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Control of Linear Servo Pneumatic Drive Based on Fuzzy Controller and Knowledge Base

  • E. L. KhazievEmail author
Conference paper
  • 66 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 641)

Abstract

The study is devoted to the control system of a linear pneumatic actuator based on fuzzy logic with the ultimate task of improving the quality of control indicators, namely the positioning accuracy at intermediate points of possible displacements, while maintaining high performance. Fuzzy inference techniques are widely used in the development of fuzzy controllers. The primary purpose of the fuzzy controller is to control an external object where the behavior of the managed object is described by fuzzy rules. Fuzzy logic controllers are the most important application of the fuzzy set theory. Their functioning differs from that of ordinary controllers by the fact the knowledge is used to describe the system instead of differential equations. The automated pneumatic actuator control system based on a fuzzy controller should be based on a knowledge base containing fuzzy frames (rules). The formation of this base is carried out on the basis of the knowledge of experts or the method of direct measurement with the help of control equipment.

Keywords

Pneumatic actuator Control system Servo mode Adaptive mode Fuzzy logic Fuzzy controller Knowledge base 

References

  1. 1.
    Lovin, D.: Create an android robot with your own hands. Publishing House DMK-Press, Moscow (2007)Google Scholar
  2. 2.
    Pashkov, E.V., Kramar, V.A., Kabanov, A.A.: Servo drives, industrial process equipment: Training manual. Lan Publishing House, St. Petersburg (2015)Google Scholar
  3. 3.
    Khaziev, E.L.: Mathematical modeling of control system of pneumatic manipulator of an industrial robot. Sci. Tech. Gaz. Volga Region 3, 173–177 (2011)Google Scholar
  4. 4.
    Khaziev, E.L.: Control system pneumatic industrial robot. Sci. Tech. Gaz. Volga Region 4, 216–222 (2012)Google Scholar
  5. 5.
    Khaziev, E.L.: The calculation of the main parameters of the crane valve system of an industrial robot. Sci. Tech. Gaz. Volga Region 4, 223–226 (2012)Google Scholar
  6. 6.
    Khaziev, E.L.: Simulation industrial pneumatic robot. In: Information Technologies. Automation. Updating and Solving Problems of Training of Highly Qualified Personnel, pp. 230–238 (2014)Google Scholar
  7. 7.
    Isaev, G.N.: Design of information systems: studies. Publishing House Omega-L, Moscow (2013)Google Scholar
  8. 8.
    Nikolenko, S., Kadurin, E., Arkhangel’skaya, E.: Deep Learning. Piter, St. Petersburg (2018)Google Scholar
  9. 9.
    Khaziev, E.L., Dmitriev, S.V.: Pneumatic dispenser. RU Patent 158927, 20 October 2016 (2016)Google Scholar
  10. 10.
    Litvinenko, A.M., Vasiliev, M.A.: Industrial robot with parallel kinematic chains. Mach. Eng., 46–48 (2007)Google Scholar
  11. 11.
    Kaliaev, I.A., Lokhin, V.M., Makarov, I.M., et al.: Intelligent robots: textbook for universities. Mashinostroenie, Moscow (2007)Google Scholar
  12. 12.
    Akimenko, T.A., Arshakyan, A.A., Budkov, S.A., et al.: Industrial robot with a management information system. Proc. Tula State Univ. 4, 133–138 (2013)Google Scholar
  13. 13.
    Yarushkina, N.G.: The foundations of fuzzy and hybrid systems: study guide. Finance and Statistics, Moscow (2004)Google Scholar
  14. 14.
    Yarushkina, N.G.: Applied intelligent systems based on soft computing. ULSTU, Ulyanovsk (2004)Google Scholar
  15. 15.
    Khaziev, E.L., Khaziev, M.L.: Control system pneumatic robot based on fuzzy logic. Mod. High Technol. 3(1), 74–78 (2016)Google Scholar
  16. 16.
    Khaziev, E.L., Khaziev, M.L.: Fuzzy control for pneumatic feed milling-boring machine with the use of the XML specification. Mod. High Technol. 9(1), 84–88 (2016)Google Scholar
  17. 17.
    Khaziev, E.L.: Fuzzy control of pneumatic subsystems of the machine tool during loading and unloading operations. Dissertation, Naberezhnye Chelny (2017)Google Scholar
  18. 18.
    Gavrilova, T.A., Khoroshevskiy, V.F.: Knowledge base of intelligent systems. Piter, St. Petersburg (2000)Google Scholar
  19. 19.
    Zubkov, E.V., Dmitriev, S.V., Khayrullin, A.K.: Algorithmization of technological processes automated testing of diesel engines. Kazan University, Kazan (2011)Google Scholar
  20. 20.
    Solodovnikov, I.V., Rogozin, O.V., Shuruev, O.V.: Implementing logical inference mechanism for the prototype expert system. New Information Technologies, Moscow (2004)Google Scholar
  21. 21.
    Batyrshin, I.Z., Nedosekin, A.O., Stecko, A.A.: Fuzzy hybrid system. Theory and practice. Fizmatlit, Moscow (2007)Google Scholar
  22. 22.
    Leonenkov, A.V.: Fuzzy modeling in MATLAB and fuzzyTECH. BKhV Petersburg, St. Petersburg (2005)Google Scholar
  23. 23.
    Sidnyaev, N.I.: Theory of experiment planning and statistical data analysis. Urait, Moscow (2012)Google Scholar
  24. 24.
    CAMOZZI: Pneumatics for everyone. Tutorial Company (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Naberezhnye Chelny InstituteNaberezhnye ChelnyRussian Federation

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