The buildup of thermodynamic cycle parameters is the main way to increase gas turbine engine efficiency. However, the growth of engine pressure and temperature ratio leads to the increase in the turbine heat load, which reduces the engine lifetime dramatically. In terms of gas turbine engines, to avoid the engine life loss is a crucial problem especially for small engines, because the limited size of a small gas turbine engine does not allow implementing various measures for nozzle vane cooling. In light of this, the contribution of the turbine heat control is essentially increasing. It places great demands on the accuracy of control over the main engine variables (such as the rotor speed and turbine outlet temperature). The state-of-the-industry gas turbine engines use an on-board engine mathematical model to improve the quality of the control. These models deal with engine processes of short duration and considerable overshooting. For that reason, the model accuracy is the main aspect in the control process. However, the issues of accurate and at the same time resource-saving calculation of rapidly varying processes of changing the rotor speed and the turbine gas temperature remain under-investigated. In the work, neural network methods were used to model the unsteady modes of a small gas turbine engine. Using the data obtained as a result of firing tests of the JetCat P-60 engine, the engine regression neural network model was created. The main issue that arose during the creation of the model was to describe the dynamics of rapidly varying processes with pronounced overshoot. For this purpose, modification of the architecture of the classical LSTM network was carried out, the essence of which was to add a functional dependence of the exit node on the memory tensor. This allowed us to make the memory size independent of the number of model outputs, thereby increasing the modeling accuracy. The developed architecture was proposed a new name - VMLSTM network. As a result of comparison with the traditional Elman network and the classic LSTM network, the developed VMLSTM network showed the least value of the average error with a comparable number of modifiable model parameters. In addition, unlike the existing neural networks, the developed network demonstrated the ability to simulate turbine outlet gas over-temperature at the moments when the engine operating mode changes. The developed neural network architecture increases the reliability of modeling the dynamics of a small gas turbine engine as an object of control, which in the conditions of economical use of computing resources opens up possibilities of its application in on-board microcomputers.

The study covers the development of a mathematical model of a micro gas turbine (MGTE) operating under transient conditions using a recurrent neural network. The compressor inlet temperature and pressure depending on the aircraft height and speed are taken into account explicitly. A full-size mathematical dynamic MGTE model based on engine per-unit description was used to verify the developed model. The obtained model was compared with the existing one employing normalized parameters of aircraft flight level and airspeed. The simulation suggests that the proposed model yields significantly smaller errors than the existing one, whereas the computation time of both models differs insignificantly.

Latterly, neural networks have been used to simulate gas turbine engine dynamics and regulator synthesis. However, little attention has been paid to structure rationalizing of the neural network for identification problems. In addition, neural networks are often used for control issues. Usually these problems are tuning PID regulator coefficients or control in the certain modes. Therefore, it is important to study of the neural network structure dependence on the simulated engine parameter and the neurocontroller synthesis to control the engine in all modes. The neural network architecture was studied for rotor frequency parameter based on the engine tests. In the result neurocontroller was synthesed based on the JetCat P-60 SE engine model taking into account the limitations of the engine fuel consumption. The results allow us to reduce the total time to model engine and synthesys nonlinear controller.