Computing safe parameters of maneuver commencing of aerobatics aircraft using artificial neural network

Aeronautical and Space-Rocket Engineering

Dynamics, ballistics, movement control of flying vehicles


DOI: 10.34759/vst-2020-2-169-184

Аuthors

Ied K. 1*, Maslennikova G. E.2**, Tiumentsev Y. V.1***

1. Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, Moscow, А-80, GSP-3, 125993, Russia
2. ,

*e-mail: Kaissied@mai.ru
**e-mail: maslennikova@gosniiga.ru
***e-mail: yutium@gmail.com

Abstract

The article considers artificial neural networks employed for sporting aircraft maneuvers computing method developing. System approach, describing it in the form of the MPL neuron network, is used for representation of such network. As long as initial training data represent complex functional dependencies with the number of variables greater than two, conventional approximation methods application is complicated. Thus, neural network modelling was employed for the problem solution. The concept of neuron represents the basis of neuron representation of aircraft flight trajectories (in the context of movement determining for an AIRCRAFT, and in the context of detecting and tracking devices). Correction of the MPL network architecture structure means the number of hidden layers and neurons (nodes) in each layer. Activation functions for each level are selected at this stage as well, i.e. they are assumed to be known. Weights and deflections are the unknown parameters with should be evaluated. Whereas excitations from the other neurons are fed to the input. For practical implementation of this approach a mathematical model of the Yak-55M sporting aircraft kind was developed on the X-Plane flight simulator using an algorithm of the training cycle of the network of multi-layer perceptron. The article presents also simulation results of the set problem on computing the safe parameters of a sporting aircraft maneuver starting. The study demonstrates that the neural network properties, such as non-linearity and good generalization ability, enhance its ability for complex tasks learning and can produce correct result for new initial data. The aircraft under analysis is out of effective system for collisions with ground prevention based on the predicted course of evasive manoeuver. However, the problem can be solved by developing relationship between the piloting errors and flight safety, and employing neuron network modelling for a number of maneuvers, which associate velocity and altitude parameters and automatically compared with the preset values. The model demonstrated the results of the sporting aircraft maneuvering starting parameters computing. With this, the probability of reliability of a great number of maneuvers should correspond to the reality. The results obtained while mathematical modelling should be loaded to the warning system to warn the pilot on the maneuver performing at the inappropriate altitude, and offer the recovery from the manoeuver allowing secure the flight and minimize hazardous situation.

Keywords:

aerobatics aircraft, prevention of flight accidents, delay of recovery from a maneuver, safe parameters of a maneuver commencing, warning system, multi-layer perceptron, neural network

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