Modeling and analysis of the dynamics of guidance and flight processes of a guided anti-aircraft missile using artificial neural networks

The article presents the application of artificial neural networks in modeling the dynamics of the guidance and flight process of an anti-aircraft missile. Results from combat firing tests of anti-aircraft missile systems were used. The results of studies enabling the assessment of the accuracy of object representation by the designated model are included.Modeling the flight dynamics of guided aerial objects is currently a subject of interest for numerous research centers. Mathematical and simulation models are used, among others, in simulators for training pilots, combat crews of radars and anti-aircraft systems, Air Force command post personnel, as well as in investigations into the causes of aviation accidents [1-4,7-12].
At the Air Force Institute of Technology, models of the flight dynamics of guided flying objects have been developed for many years based on flight measurement results using computer identification methods [3,7-10]. In this methodology of model development, the essence is the selection of the form of the equation defining the values of aerodynamic forces and moments acting on the object depending on motion and control parameters. The values of the coefficients of these equations are determined by the aforementioned identification methods. In the general case, the mentioned equations represent aerodynamic forces and moments in a limited range of the object’s full operational envelope. (…)

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