SELECTED AIRCRAFT THROTTLE CONTROLLER
WITH SUPPORT OF FUZZY EXPERT
INFERENCE SYSTEM
ROZMYTY SYSTEM WSPOMAGANIA
STEROWANIEM DŹWIGNIĄ STEROWANIA
SILNIKIEM WYBRANEGO STATKU POWIETRZNEGO
Józef Żurek
1), Norbert Grzesik
2), Jakub Kurpas
3) 1)Air Force Institute of Technology in Warsaw 2)Aviation Faculty, Polish Air Force Academy in Dęblin
3)
First Officer in Enterair Airlines
e-mail: jozef.zurek@itwl.pl, norbertgrzesik@o2.pl, jakub.kurpas@gmail.com Abstract: The paper describes Zlin 143Lsi aircraft engine work parameters control support method – hourly fuel flow as a main factor under consideration. The method concerns project of aircraft throttle control support system with use of fuzzy logic (fuzzy inference). The primary purpose of the system is aircraft performance optimization, reducing flight cost at the same time and support proper aircraft engine maintenance. Matlab Software and Fuzzy Logic Toolbox were used in the project. Work of the system is presented with use of twenty test samples, five of them are presented graphically. In addition, system control surface, included in the paper, supports system all work range analysis.
Keywords:throttle controller, fuzzy logic, fuzzy expert inference system, support of control Streszczenie: W artykule przedstawiono metodę wspomagania zarządzania parametrami pracy silnika samolotu Zlin 143Lsi, z naciskiem na przepływ paliwa na godzinę. Metoda ta polegała na zaprojektowaniu układu wspomagania wykorzystującego wnioskowanie rozmyte. Głównym zadaniem układu jest zoptymalizowanie osiągów samolotu, poprzez redukcję kosztów lotu, a także zapewnienie poprawnej eksploatacji silnika. W projekcie wykorzystano oprogramowanie Matlab i Fuzzy Logic Toolbox. Zaprezentowano pracę układu w oparciu o dwadzieścia próbek testowych, z których pięć zobrazowano graficznie. Dodatkowo przedstawiono płaszczyznę sterowania układu, która umożliwia analizę pracy tegoż układu w całym jego zakresie.
Słowa kluczowe: dźwignia sterowania silnikiem, logika rozmyta, rozmyty, ekspercki system wnioskowania, wspomaganie sterowania
1. Introduction
The main type of complex plane which is used for the Instrument Rating Training is a Zlin 143Lsi aircraft. Equipped with modern Garmin G950 avionics system and tanks with a capacity of 220 liters, gives the possibility of long flights even in severe weather conditions. The most common problem for students is the optimal selection of the engine operating parameters, mainly manifold pressure and rotational speed of the propeller, which directly translates into the hourly fuel consumption. The aim of the project is to develop the fuzzy expert inference system supporting the engine operating parameters management, with focus on the flow of fuel per hour. Such a device will give student pilot a complete picture of fuel usage, allowing him to optimize performance of the aircraft, reducing the cost of the flight and also provides the proper engine exploitation. Fuzzy throttle controller will be system base, which external display panel, with processed data, could be similar to presented on figure 1.
Fig. 1 Common visualization of engine parameters for a complex piston aircraft
2. Description of the model
Zlin 143Lsi aircraft is powered by six-cylinder, four-stroke air-cooled fuel-injected TEXTRON Lycoming IO-540-C4D5 engine, with the maximum continuous power of 235 HP at 2400 rev/min. The engine does not have a speed reducer and is not turbo-charged. Basic adjustable motor parameters is the power (manifold pressure), propeller speed and fuel mixture, adjustable separately but not independently.
Bendix RSA fuel injection system is based on the measurement of air flow which is used by the transducer converting the air pressure to the fuel pressure. The data obtained by the fuel pressure measuring device determines the correct amount of fuel injected relative to the amount of air. The fuel evaporates in the engine cylinder intake valve.
The MTV-9-B/195-45a is three-blade constant speed, hydraulically controlled with variable pitch propeller. Changing the pitch of the propeller blades is made by a speed regulator, which maintains propeller speed regardless of the speed of aircraft and engine power. The propeller pitch range change mechanism is limited by intermittent high/low pitch movement mechanisms. In case of loss of oil pressure, the propeller blades are set automatically at a low pitch. Oil pressure in reducer is one-sided, in the high pitch sense. Low pitch is changed by aerodynamic forces that act on the blade.
The integrated Garmin G950 avionics is a flight control system integrating instrumentation, engine, navigation, communications and surveillance. The main elements of the system are: the ADC on-board computer, engine and GEA processor sensors and integrated GIA avionics.
To determine the optimal flow of fuel during the flight, a fuzzy expert inference system was designed in Matlab Fuzzy Logic Toolbox. The system consists of two input signals, which are the power setting (manifold pressure, four, triangular membership functions) and the propeller speed (three, trapezoidal membership functions). The system output signals is expressed as: econ 55%, 65% econ, cruise, fast cruise, max (five, triangular membership functions).
Zlin 143Lsi fuzzy throttle controller is presented on figure 2. Membership functions for inputs and output are presented on figure 3 and 4 below.
Fig. 3 Fuzzy throttle controller inputs membership functions
Fig. 4 Fuzzy throttle controller output membership functions
Presented engine parameters correspond to the numerical value for the range of normal operation and were taken from the Zlin 143 Lsi Flight Manual, Document No. Si005.012.G, sections; Normal Procedures and Performance.
Fig.5. Fuzzy throttle controller rule base
3. Testing of the correct program work
In order to verify the correctness of the calculation of the model random samples were taken. On this basis, the performance evaluation of the program has been made (table 1).
Tab. 1 Fuzzy controlling testing samples
Nu. of the sample Propeller speed [r/min] Manifold pressure [inch Hg] Fuel flow [l/h] 1. 2100 16 33,2 2. 2100 18 33,2 3. 2100 20 40 4. 2100 22 45 5. 2100 24 50 6. 2300 16 40 7. 2300 18 50 8. 2300 20 60 9. 2300 22 61,3 10. 2300 24 66,8 11. 2250 22 55 12. 2250 24 60 13. 2150 16 33,2 14. 2150 18 40 15. 2150 20 50 16. 2150 22 55 17. 2150 24 60 18. 2250 16 33,2 19. 2250 18 40 20. 2250 20 50
Calculated (defuzzified) results of fuel flow are presented below (five selected results).
Fig. 6 Sample 1 calculation result
Fig. 8 Sample 7 calculation result
Fig. 10 Sample 16 calculation result
Fuzzy expert inference system control surface is presented on figure 11.
Fig. 12 Detailed relationships betweenfuel flow-propeller speed and fuel flow-manifold pressure
4. Conclusion
After analyzing the results and comparing simulation outputs with their counterparts it can be concluded that the program fairly accurately determines the level of fuel consumption. However, do not overlook the fact that its action is based on certain assumptions and do not reflect the actual situation in one hundred percent. Still, without taking into account the situation of extreme and marginal, based on average values and keeping in mind the adopted simplifications and assumptions can be useful to assess the program, aptly optimizing hourly fuel consumption during the flight. The results coincide with the recommendations issued by the manufacturer, posted in table 2 below.
Tab. 2 Zlin 143 Lsi aircraft manufacturer recomendations
Power Density altitude Propeller speed Manifold pressure Fuel
Flow CAS Range Endurance [ft] [r/min] [inch Hg] [l/h] [kts] [NM] [h;mm] MT 0 2400 max 96,0 140 259 1;50 MT 2000 2400 max 85,0 135 286 2;05 MT 4000 2400 max 74,5 129 319 2;20 MT 6000 2400 max 64,0 124 367 2;45 MT 8000 2400 max 53,0 118 437 3;20 MT 10000 2400 max 42,5 113 529 4;10 MC 0 2200 25,0 55,5 125 400 3;10 MC 2000 2200 25,0 56,3 125 400 3;05 MC 4000 2200 25,0 57,0 125 400 3;00 MC 6000 2200 Max 51,0 120 443 3;25 MC 8000 2200 Max 45,0 114 497 3;55 MC 10000 2200 Max 39,0 109 562 4;30 EC 0 2000 24,5 43,0 113 464 4;05 EC 2000 2000 24,5 43,4 113 470 4;00 EC 4000 2000 24,5 43,5 113 475 4;00 EC 6000 2000 max 40,5 110 513 4;15 EC 8000 2000 max 37,0 105 551 4;40 EC 10000 2000 max 33,5 100 599 5;10
5. References
[1] Aircraft General Knowledge, Powerplant, Nordian ISBN: 978-82-8107-107-9. [2] Azadeh A.; Ebrahimipour V.; Bavar P.: A fuzzy inference system for pump
failure diagnosis to improve maintenance process: The case of a petrochemical industry. University of Teheran 2009.
[3] Grzesik N.: Podstawy sterowania rozmytego. WSOSP. Dęblin 2012. [4] Mrozek B.; Mrozek Z.: Matlab 6 - Poradnik użytkowania. Wydawnictwo
PLJ. Warszawa 2001.
[5] Piegat A.: Modelowanie i sterowanie rozmyte. Akademicka Oficyna Wydawnicza. Warszawa 1999.
[6] Sergaki A.; Kalaitzakis K.: A fuzzy knowledge based method for maintenance
planning in a power system. Technical University of Crete 2001.
[7] Yager R.; Filev D.: Podstawy modelowania i sterowania rozmytego. WNT. Warszawa 1995.
[8] Zadeh L.: Fuzzy Sets. Information and Control Vol.8. 1965. [9] Zlin 143 Lsi Manual, Serial Si005.012.G.
Profesor dr hab. inż. Józef ŻUREK, prof. zwyczajny Instytutu Technicznego Wojsk Lotniczych w Warszawie, przewodniczący rady Naukowej ITWL. Ukończył Wojskową Akademię Techniczną w 1969r.; Inicjator, a także współtwórca wdrożonych w Siłach Zbrojnych RP systemów ewidencji i przetwarzania danych eksploatacyjnych wojskowych statków powietrznych, które służą do analizy niezawodności, oceny trwałości techniki lotniczej oraz bezpieczeństwa lotów. Opublikował wiele prac z dziedziny eksploatacji obiektów technicznych i bezpieczeństwa systemów technicznych. Jest autorem i współautorem kilku publikacji książkowych. Uczestniczył w tworzeniu nowych kierunków naukowych w Polsce jako organizator i współorganizator wielu konferencji z dziedzin: inżynierii systemów, bezpieczeństwa systemów, eksploatacji obiektów technicznych. Odznaczony Krzyżem Kawalerskim Odrodzenia Polski.
Lt. col. Norbert Grzesik PhD Eng. Polish Air Force Academy in Dęblin. He received the Ph.D. degree in machines construction and maintenance from Rzeszow University of Technology, Rzeszow, Poland, in 2006. His specialization is using of fuzzy expert inference systems application in aircraft (military and civilian) on-board reliability and efficiency evaluation systems. Member of European Society for Fuzzy Logic and Technology (EUSFLAT) and Aviation Scientific Council.
Jakub Kurpas (M.Sc.), graduated from Polish Air Force Academy in Dęblin, member of Gliders Junior National Team in 2013. First Officer of Boeing 737 aircraft in Enterair Airlines.
