Uyarlamalı Sinirsel Bulanık Çıkarım Sistemi ile Hava Muharebesinin Gerçekleştirilmesi

Abstract

Autonomous control of aerial vehicle is a non-linear problem that requires low level robust control of many parameters. There are solutions to control an unmanned aircraft to follow a flight path, take-off and landing. In case of close range air combat there are additional objectives like preserving aircraft energy, getting to an advantageous position over the opponent, consider controlling of relative variables, instantly changing trajectory requirements. This makes control problem domain specific and very complex. With fully autonomous aerial vehicles man-kind can also take advantage of using UAV for air combat. While air combat is a dangerous, expensive and difficult activity, computers can be trained by using human combat fighters experience with machine learning techniques. Computers can also be used to support pilot training process. In this thesis, a step by step methodology is proposed to train an unmanned aircraft to fight on behalf of human. An abstraction stack is defined to isolate low level robust control of aircraft from flight intelligence. This approach allows us to focus on air combat problem independent from flight control techniques. A technique is defined to decompose complicated and hard to process flight information into machine and human readable and easily understandable format. This technique eases processing of huge amount of flight data, decreases the number of control parameters and brings a common understanding of aircraft maneuvering. The technique also includes indexing and search mechanism on flight language. It is shown how to compose air combat maneuvers using flight language. A machine learning corpus data is composed from real F-16 flight information including relative geometry and maneuver identification methodology. The sample data is not a complete solution to train widely used combat maneuvers. But as a proof of concept it is shown that the technique works fine for sample scenarios. A comparison and brief information on machine learning techniques specific to close range air combat problem is given and an ANFIS design is applied as example.