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Helicopter Turboshaft Engines’ Gas Generator Rotor R.P.M. Neuro-Fuzzy On-Board Controller Development

Author

Listed:
  • Serhii Vladov

    (Department of Scientific Work Organization and Gender Issues, Kremenchuk Flight College, Kharkiv National University of Internal Affairs, 17/6, Peremohy Street, 39605 Kremenchuk, Ukraine)

  • Lukasz Scislo

    (Faculty of Electrical and Computer Engineering, Cracow University of Technology, 24, Warszawska, 31-155 Cracow, Poland)

  • Valerii Sokurenko

    (Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine)

  • Oleksandr Muzychuk

    (Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine)

  • Victoria Vysotska

    (Information Systems and Networks Department, Lviv Polytechnic National University, 12, Bandera Street, 79013 Lviv, Ukraine
    Institute of Computer Science, Osnabrück University, 1, Friedrich-Janssen-Street, 49076 Osnabrück, Germany)

  • Anatoliy Sachenko

    (Research Institute for intelligent Computer Systems, West Ukrainian National University, 11, Lvivska Street, 46009 Ternopil, Ukraine
    Department of Teleinformatics, Kazimierz Pulaski University of Radom, 29, Malczewskiego Street, 26-600 Radom, Poland)

  • Alexey Yurko

    (Department of Computer Engineering and Electronics, Kremenchuk Mykhailo Ostrohradskyi National University, 20, University Street, 39600 Kremenchuk, Ukraine)

Abstract

The work is devoted to the helicopter turboshaft engines’ gas generator rotor R.P.M. neuro-fuzzy controller development, which improves control accuracy and increases the system’s stability to external disturbances and adaptability to changing operating conditions. Methods have been developed, including improvements to the automatic control system structural diagram which made it possible to obtain the system transfer function in the bandpass filter transfer function form. The work also improved the fuzzy rules base and the neuron activation function mathematical model, which significantly accelerated the neuro-fuzzy controller training process. The transfer function frequency and time characteristics analysis showed that the system effectively controlled the engine and reduced vibration. Methods for ensuring a guaranteed stability margin and the synthesis of an adaptive filter were studied, which made it possible to achieve the system’s high stability and reliability. The results showed that the developed controller provided high stability with amplitude and phase margins, effectively compensating for changes in external conditions. Experimental studies have demonstrated that the control quality improved by 2.31–2.42 times compared to previous neuro-fuzzy controllers and by 5.13–5.65 times compared to classic PID controllers. Control errors were reduced by 1.84–2.0 times and 5.28–5.97 times, respectively, confirming the developed neuro-fuzzy controller’s high efficiency and adaptability.

Suggested Citation

  • Serhii Vladov & Lukasz Scislo & Valerii Sokurenko & Oleksandr Muzychuk & Victoria Vysotska & Anatoliy Sachenko & Alexey Yurko, 2024. "Helicopter Turboshaft Engines’ Gas Generator Rotor R.P.M. Neuro-Fuzzy On-Board Controller Development," Energies, MDPI, vol. 17(16), pages 1-45, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:4033-:d:1456223
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    References listed on IDEAS

    as
    1. Abdalla, Muftah S.M. & Balli, Ozgur & Adali, Osama H. & Korba, Peter & Kale, Utku, 2023. "Thermodynamic, sustainability, environmental and damage cost analyses of jet fuel starter gas turbine engine," Energy, Elsevier, vol. 267(C).
    2. Balli, Ozgur, 2023. "Exergetic, sustainability and environmental assessments of a turboshaft engine used on helicopter," Energy, Elsevier, vol. 276(C).
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