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Onboard Neuro-Fuzzy Adaptive Helicopter Turboshaft Engine Automatic Control System

Author

Listed:
  • Serhii Vladov

    (Kremenchuk Flight College of Kharkiv National University of Internal Affairs, 17/6 Peremohy Street, 39605 Kremenchuk, Ukraine)

  • Maryna Bulakh

    (Department of Industrial Processes Computerization and Robotization, Faculty of Mechanics and Technology, Rzeszow University of Technology, 4 Kwiatkowskiego Street, 37-450 Stalowa Wola, Poland)

  • 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)

  • Ruslan Yakovliev

    (Kremenchuk Flight College of Kharkiv National University of Internal Affairs, 17/6 Peremohy Street, 39605 Kremenchuk, Ukraine)

Abstract

A modified onboard neuro-fuzzy adaptive (NFA) helicopter turboshaft engine (HTE) automatic control system (ACS) is proposed, which is based on a circuit consisting of a research object, a regulator, an emulator, a compensator, and an observer unit. In this scheme, it is proposed to use the proposed AFNN six-layer hybrid neuro-fuzzy network (NFN) with Sugeno fuzzy inference and a Gaussian membership function for fuzzy variables, which makes it possible to reduce the HTE fuel consumption parameter transient process regulation time by 15.0 times compared with the use of a traditional system automatic control (clear control), 17.5 times compared with the use of a fuzzy ACS (fuzzy control), and 11.25 times compared with the use of a neuro-fuzzy reconfigured ACS based on an ANFIS five-layer hybrid NFN. By applying the Lyapunov method as a criterion, its system stability is proven at any time, with the exception of the initial time, since at the initial time the system is in an equilibrium state. The use of the six-layer ANFF NFN made it possible to reduce the I and II types of error in the HTE fuel consumption controlling task by 1.36…2.06 times compared with the five-layer ANFIS NFN. This work also proposes an AFNN six-layer hybrid NFN training algorithm, which, due to adaptive elements, allows one to change its parameters and settings in real time based on changing conditions or external influences and, as a result, achieve an accuracy of up to 99.98% in the HTE fuel consumption controlling task and reduce losses to 0.2%.

Suggested Citation

  • Serhii Vladov & Maryna Bulakh & Victoria Vysotska & Ruslan Yakovliev, 2024. "Onboard Neuro-Fuzzy Adaptive Helicopter Turboshaft Engine Automatic Control System," Energies, MDPI, vol. 17(16), pages 1-41, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:4195-:d:1461715
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    References listed on IDEAS

    as
    1. Maryna Bulakh & Leszek Klich & Oleksandra Baranovska & Anastasiia Baida & Sergiy Myamlin, 2023. "Reducing Traction Energy Consumption with a Decrease in the Weight of an All-Metal Gondola Car," Energies, MDPI, vol. 16(18), pages 1-12, September.
    2. Denys Baranovskyi & Maryna Bulakh & Adam Michajłyszyn & Sergey Myamlin & Leonty Muradian, 2023. "Determination of the Risk of Failures of Locomotive Diesel Engines in Maintenance," Energies, MDPI, vol. 16(13), pages 1-14, June.
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