novtexmechМехатроника, автоматизация, управлениеMekhatronika, Avtomatizatsiya, Upravlenie1684-64272619-1253Commercial Publisher «New Technologies»10.17587/mau.26.22-27novtexmech-1680Research ArticleАВТОМАТИЗАЦИЯ И УПРАВЛЕНИЕ ТЕХНОЛОГИЧЕСКИМИ ПРОЦЕССАМИ И ПРОИЗВОДСТВАМИAUTOMATION AND CONTROL TECHNOLOGICAL PROCESSESНейросетевой имитатор газотурбинных двигателей для прототипирования и отладки систем управления неустановившимися режимамиNeural Network Simulator of Gas Turbine Engines for Prototyping and Debugging of Control Systems on Unsteady ModesАбдулнагимовА. И.AbdulnagimovA. I.

канд. техн. наук, доц.

abdulnagimov@gmail.comАнтоновВ. В.AntonovV. V.

д-р техн. наук, проф.

antonov.v@bashkortostan.ruЧепайкинА. С.ChepaykinA. S.

инженер-программист

31435@mail.ruПальчевскийЕ. В.PalchevskyE. V.

канд. техн. наук, ст. преп.

teelxp@inbox.ruНасыровН. Т.NasyrovN. T.

ведущий инженер-конструктор

31435@mail.ruУфимский университет науки и технологийРоссияUfa University of Science and TechnologyRussian FederationФинансовый университет при правительстве РоссийскойРоссияFinancial University under the Government of the Russian FederationRussian FederationОАО "Уфимское агрегатное производственное объединение"РоссияJoint Stock Company "Ufa Aggregate Production Association"Russian Federation2025210120252612227Copyright © Commercial Publisher «New Technologies», 20252025Commercial Publisher «New Technologies»Commercial Publisher «New Technologies»https://mech.novtex.ru/jour/about/submissions#copyrightNoticehttps://mech.novtex.ru/jour/article/view/1680

Рассмотрен принцип создания нейросетевого имитатора газотурбинных двигателей в виде рекуррентных нейронных сетей и их применение в полунатурном моделировании для тестирования и отладки систем автоматического управления, контроля и диагностики. Проведено сравнение нейросетевых имитаторов на базе архитектур NARX и GRU. Описана методика построения нейросетевого имитатора (модели) газотурбинной установки и ее реализация на стенде полунатурного моделирования. Полунатурное моделирование используется для прототипирования, испытаний и сертификации разрабатываемых изделий (физических объектов) с комплексом математических моделей, где реальные системы сопряжены с виртуальными моделями, в составе которого они находятся. В работе представлены результаты полунатурного моделирования параметров газотурбинного двигателя с реальной системой автоматического управления, контроля и диагностики двигателя для режимов запуска, на земле и в полете. Проведен анализ точности и адекватности рассмотренных моделей. Подтверждена точность, необходимая для решения задач управления и формирования требований к агрегатам электронных систем управления. Подход на основе интеллектуального моделирования может быть использован для создания полноценных (комплексных) цифровых двойников, где модель физических процессов и поведения объекта на основе рекуррентных нейронных сетей может быть подключена к трехмерному твердотельному моделированию для решения задач анализа и синтеза объекта, его оптимизации и повышения надежности. Развитие таких технологий позволяет создавать интеллектуальные модели, которые могут быть использованы в цифровых двойниках сложных технических систем

The principle of creation of neural network simulator of gas turbine engines in the form of recurrent neural networks and their application in the hardware-in-the-loop simulation for testing and debugging automatic control and condition-monitoring systems is considered. A comparison of NARX and GRU architectures of simulators is carried out. A technique for constructing a gas turbine neural network simulator (model) and its implementation on a hardware-in-the loop simulation testbed is described. Hardware-in-the loop simulation is used for prototyping, testing and certification of developed products (physical objects) with a complex of mathematical models, where real systems are associated with virtual models in which they are located. The results of hardware-in-the loop simulation of the parameters of a gas turbine engine with a real full authority digital engine control system for startup mode, ground mode and flight modes are presented. An analysis of the accuracy and adequacy of the considered models is carried out. The accuracy required to solve control problems and form requirements for electronic control systems units has been confirmed. The intelligent modeling approach can be used to create full-fledged (complete) digital twins, where a model of physical processes and object behavior based on recurrent neural networks can be connected to 3D solid-state modeling to solve problems of object analysis and synthesis, its optimization and reliability increase. The development of such technologies makes it possible to create intelligent models that can be used in digital twins of complex technical systems

газотурбинный двигательрекуррентная нейронная сетьGRUNARXдинамическая модельаппаратно-программное моделированиеgas turbine enginerecurrent neural networkGRUNARXdynamic modelhardware-in-the loop simulationИсследование выполнено при финансовой поддержке Министерства науки и высшего образования Российской Федерации в рамках основной части государственного задания высшим учебным заведениям № FEUE-2023-0007The research was supported by the Ministry of Science and Higher Education of the Russian Federation within the framework of the State Assignments "Development of critical technologies for creating power plants for small and regional aviation, as well as unmanned aircraft systems" № FEUE-2023-0007ReferencesProkhorov A., Lysachev M., Borov kov A. Digital twin. Analysis, trends, world experience, Moscow, LLC "AlliancePrint", 2020. 401 p. (in Russian).Prokhorov A., Lysachev M., Borov kov A. Digital twin. Analysis, trends, world experience, Moscow, LLC "AlliancePrint", 2020. 401 p. 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The authors declare that there are no conflicts of interest present.