Multi-agent control of the combustion chamber oxidizer using neuro-fuzzy technology in the MISO system

DOI: 10.34759/trd-2022-124-21

Аuthors

Storozhev S. A.

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

Abstract

The an aircraft gas turbine engine (GTE) of aircraft from the point of view of control theory is a complex nonlinear object, the frame mathematical description of which is known a priori, has one input and several outputs. When operating a gas turbine engine, continuous monitoring of parameters is required (gas temperature behind the combustion chamber, rotor speed of the low-pressure compressor (free turbine), rotor speed of the high-pressure turbocharger (gas generator). The selective controller controls these parameters, realizing control in different modes of operation of the gas turbine engine. Further The development of GTE control can be associated with the use of neuro-fuzzy control of the oxidizer flow into the GTE combustion chamber. Purpose: improving the control of the combustion process in the combustion chamber of the gas turbine engine. Methods: a new approach to designing an adaptive state controller based on neuro-fuzzy technology using triangular terms with equal bases equal to the interval 0–1, whose vertices are shifted according to the arithmetic mean of the path traveled by the input variables, is proposed. Determination of the degree of membership is performed by singletons, synchronously shifting depending on the change in input parameters using proportions. On the basis of the proposed approach to designing an adaptive state controller with an oxidizer flow, the relationship between the input parameters of the combustion chamber and the development of the control control with maximum speed is estimated using the area ratio method or the weighted average method. The idea of the experiment, using the designed state controller, is to estimate the temperature change behind the combustion chamber, which should not leave the specified zone. Results: the developed adaptive state controller is characterized by the best values of the probability of non-failure operation during the experiment. Practical relevance: the research results can be used in the control of the combustion chamber. This can significantly reduce the uncertainty in the operation of the combustion chamber, ensuring a minimum release of harmful substances and guaranteed thrust of the aircraft.

Keywords:

aircraft gas turbine engine, combustion chamber, electronic engine controller, neuro-fuzzy state controller, area difference method, weighted average method, fuzzifier, defuzzifier, external guide vanes

References

1. Inozemtsev A.A., Nikhamkin V.L., Sandratskii A.A. Osnovy konstruirovaniya aviatsionnykh dvigatelei i energeticheskikh ustanovok (Fundamentals of the development of aircraft engines and power plants), 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. Pismenniy V.L. Trudy MAI, 2002, no. 8, URL: https://trudymai.ru/eng/published.php?ID=34593
4. Gurevich O.S. Sistemy avtomaticheskogo upravleniya GTD: Entsiklopedicheskii spravochnik (GTE automatic control systems: Encyclopedic reference book), Moscow, TORUS PRESS, 2011, 208 p.
5. Sinyakov A.N., Shaimardanov F.A. Sistemy avtomaticheskogo upravleniya LA i ikh silovymi ustanovkami (Automatic control systems for aircraft and their power plants), Moscow, Mashinostroenie, 1991, 320 p.
6. Isaev A.I., Skorobogatov S.V. Trudy MAI, 2018, no. 98. URL: http://trudymai.ru/eng/published.php?ID=87340
7. Khizhnyakov Yu.N. Nechetkoe, neironnoe i gibridnoe upravlenie (Fuzzy, neural and hybrid control: textbook), Perm’, Izd-vo PNIPU, 2013, 303 p.
8. Pegat A. Nechetkoe modelirovanie i upravlenie (Fuzzy modeling and control), Moscow, BINOM. Laboratoriya znanii, 2007, 798 p.
9. Andrievskaya N.V., Andrievskii O.A., Legotkina T.S., Khizhnyakov Yu.N., Storozhev A.A., Nikulin V.S., Yuzhakov A.A., Kuznetsov M.D. Mekhatronika. Avtomatizatsiya. Upravlenie, 2020, vol. 21, no. 6, pp. 348-355. DOI: 10.17587/mau.21.348-355
10. Zhukovskaya E.P., Lebedev M.V. Aviakosmicheskoe priborostroenie, 2002, no. 3, рр. 40-44.
11. Gostev V.I. Proektirovanie nechetkikh regulyatorov dlya sistem avtomaticheskogo upravleniya (Design of fuzzy controllers for automatic control systems), Saint Petersburg, BKhV-Peterburg, 2011, 416 p.
12. Gostev V.I. Sistemy upravleniya s tsifrovymi regulyatorami. Spravochnik (Сontrol with digital controllers. Directory), Kiev, Tekhnika, 1990, 280 p.
13. Yarushkina N.G. Osnovy teorii nechetkikh i gibridnykh system (Fundamentals of the theory of fuzzy and hybrid systems), Moscow, Finansy i statistika, 2004, 320 p.
14. Bobyr’ M.V., Kulabukhov S.A. Vestnik komp’yuternykh i informatsionnykh tekhnologii, 2015, no. 9, pp. 32–41.
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. Shtovba S.D. Proektirovanie nechetkikh sistem sredstvami MATLAB (Designing fuzzy systems using MATLAB), Moscow, Goryachaya liniya.Telekom, 2007, 288 p.
18. Khizhnyakov Yu.N., Yuzhakov A.A. Pribory, 2010, no. 5, pp. 17-21.
19. Chicherova E.V. Trudy MAI, 2015, no. 81. URL: http://trudymai.ru/eng/published.php?ID=57812
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