A new status regulator adapting method employing fuzzy logic


DOI: 10.34759/trd-2021-118-16

Аuthors

Storozhev S. A., Khizhnyakov Y. N.

Perm National Research Polytechnic University, PNRPU, 29, Komsomolsky Prospekt, Perm, 614990, Russia

Abstract

From the control theory point of view, an aircraft gas turbine engine (GTE) is a complex nonlinear object, which frame mathematical description is known a priori. While the GTE operation, continuous parameters monitoring, such as gas temperature behind the combustion chamber, rotor speed of a low pressure compressor (free turbine), rotor speed of a high pressure turbocharger (gas generator), is required. Further development of gas turbine engine control may be associated with the fuzzy control application. The goal of the study consists in upgrading control the fuel supplying to the GTE combustion chamber. The article proposes a new approach to the state regulator adaptation employing triangular terms with different bases, which vertices are being displaced depending on the arithmetic mean values of the input variables of the gas turbine engine, and the bases of the terms are attached to the current abscissas of the term vertices. There is a possibility to develop an adaptive fuzzifier from the analysis of the «traversed path» of each input variable, based on the proposed approach to terms adaptation. The grade of membership determining of the fuzzyfier was performed on the singleton base. Defuzzyfication was performed based on the weight-average formula. This adaptive state regulator allows replacing the standard selector and ensuring adaptability to the GTE external operation conditions. The developed adaptive state regulator is being characterized by better values probability of no-failure of the engine electronic regulator (EER). The results of the study may be employed for the combustion chamber control. The obtained adaptive state regulator will allow significant reduction of the uncertainty in the combustion chamber operation, ensuring guaranteed thrust of the flying vehicle.

Keywords:

aircraft gas turbine engine, adaptive fuzzy status regulatoraz@q, weighted average method, adaptive fuzzyfier, defuzzyfier, dispenser

References

  1. Inozemtsev A.A., Nikhamkin A.A., Sandratskii V.L. Osnovy konstruirovaniya aviatsionnykh dvigatelei i energeticheskikh ustanovok (Fundamentals of aircraft engines and power plants design), Moscow, Mashinostroenie, 2008, vol. 2, 368 p.

  2. Kostyukov V.M., Kapyrin N.I. Trudy MAI, 2011, no. 49. URL: http://trudymai.ru/eng/published.php?ID=28075

  3. Gurevich O.S. Upravlenie aviatsionnymi gazoturbinnymi dvigatelyami (Aircraft gas turbine engine control), Moscow, Izd-vo MAI, 2001, 100 p.

  4. Gurevich O.S. Sistemy avtomaticheskogo upravleniya GTD: entsiklopedicheskii spravochnik (Automatic control systems for gas turbine engines: Encyclopedic reference book), Moscow, Torus Press, 2011, 208 p.

  5. Borovikov D.A., Ionov A.V., Seliverstov S.D., Yakovlev A.A. Trudy MAI, 2017, no. 96. URL: http://trudymai.ru/eng/published.php?ID=85654

  6. Dorf R., Bishop R. Sovremennye sistemy upravleniya (State-of-the-art control systems), Moscow, Laboratoriya bazovykh znanii, 2002, 832 p.

  7. Khizhnyakov Yu.N. Nechetkoe, neironnoe i gibridnoe upravlenie (Fuzzy, neural and hybrid control: textbook. guide), Perm’, Izd-vo PNIPU, 2013, 303 p.

  8. Gostev V.I. Proektirovanie nechetkikh regulyatorov dlya sistem avtomaticheskogo upravleniya (Fuzzy controllers design for automatic control systems), Saint Petersburg, BKhV-Peterburg, 2011, 416 p.

  9. Gostev V.I. Sistemy upravleniya s tsifrovymi regulyatorami: spravochnik (Control systems with digital regulators. Directory), Kiev, Tekhnika, 1990, 280 p.

  10. Pegat A. Nechetkoe modelirovanie i upravlenie (Fuzzy modeling and control), Moscow, BINOM. Laboratoriya znanii, 2007, 798 p.

  11. Shtovba S.D. Proektirovanie nechetkikh sistem sredstvami MATLAB (Fuzzy systems design with MATLAB), Moscow, Goryachaya liniya — Telekom, 2007, 288 p.

  12. Leonenkov A.V. Nechetkoe modelirovanie v srede MATLAB i fuzzyTECH (Fuzzy modeling in MATLAB and fuzzyTECH), Saint Petersburg, BKhV Peterburg, 2005, 736 p.

  13. Yarushkina N.G. Osnovy teorii nechetkikh i gibridnykh system (Fundamentals of fuzzy and hybrid systems theory), Moscow, Finansy i statistika, 2004, 320 p.

  14. Chicherova E.V. Trudy MAI, 2015, no. 81. URL: http://trudymai.ru/eng/published.php?ID=57812

  15. Mamdani E.H. Application of fuzzy algorithms for the control of a simple dynamic plant, Proceedings of the Institution of Electrical Engineers, 1974, vol. 121, no.12, pp. 1585 — 1588.

  16. Antonov V.N., Terekhov V.A., Tyukin I.Yu. Adaptivnoe upravlenie v tekhnicheskikh sistemakh (Adaptive control in technical systems), Saint Petersburg, Izd-vo Sankt-Peterburgskogo universiteta, 2001, 244 p.

  17. Bobyr’ M.V., Kulabukhov S.A. Vestnik komp’yuternykh i informatsionnykh tekhnologii, 2015, no. 9, pp. 32 — 41. DOI: 10.14489/vkit.2015.09.pp.032-041

  18. Khizhnyakov Yu. N. Yuzhakov A.A. Pribory, 2010, no. 5, pp. 17 — 21.

  19. Isaev A.I., Skorobogatov S.V. Trudy MAI, 2018, no. 98. URL: http://trudymai.ru/eng/published.php?ID=90175

  20. Vereshchikov D.V., Voloshin V.A., Ivashkov S.S., Vasil’ev D.V. Trudy MAI, 2018, no. 99. URL: http://trudymai.ru/eng/published.php?ID=91926


    Download

mai.ru — informational site MAI

Copyright © 2000-2024 by MAI

 Вход