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

Serhii Vladov, Lukasz Scislo (), Valerii Sokurenko, Oleksandr Muzychuk, Victoria Vysotska, Anatoliy Sachenko and Alexey Yurko
Additional contact information
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
Anatoliy Sachenko: Research Institute for intelligent Computer Systems, West Ukrainian National University, 11, Lvivska Street, 46009 Ternopil, Ukraine
Alexey Yurko: Department of Computer Engineering and Electronics, Kremenchuk Mykhailo Ostrohradskyi National University, 20, University Street, 39600 Kremenchuk, Ukraine

Energies, 2024, vol. 17, issue 16, 1-45

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.

Keywords: gas generator rotor R.P.M.; helicopter turboshaft engines; transfer function; automatic control system; neuro-fuzzy controller; training (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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