AUTHOR(S)
 I.M. Ismayilov,  T.V. Binnataliyeva 

ABSTRACT
The article presents the principles of creating a predictive control strategy based on a neural
network model of aviation systems. The use of neural networks for identifying and controlling dynamic
systems is substantiated, including the use of model predictive control for predicting the state (parameters)
of an aircraft as a dynamic system, more suitable among neural network architectures. For model
predictive control, a model of an object is used to predict the future behavior of the object, and an
optimization algorithm is used to select a control input that optimizes the future operation of the object. To
reflect the dynamics of the system in real time, the issues of training a neural network were considered, for
which a standard model, the NARMA model, was chosen as the structure of the model.

Key words:       flight safety, flight dynamics, aviation intelligent control systems, predictive control,
neural networks, static methods, regression analysis.