Akademik Veri Yönetim Sistemi
The 5th International Conference on Materials Chemistry and Environmental Engineering (CONF-MCEE 2025), 17 Ocak 2025, (Özet Bildiri)
The ability to fly autonomously is achieved through the design of automatic controllers (i.e., autopilots). Numerous linear and non-linear methods have been developed to design automatic controllers. With its recent rapid development, artificial intelligence has also reached a level where it can perform automatic control tasks. Recently, the field of deep learning, a subset of artificial intelligence, has enabled machines to learn various behaviors. This paper discusses the application of artificial intelligence technology in the context of the automatic control of fixed-wing aerial vehicles. Despite the reliance of both linear and non-linear control methodologies on the dynamic model of the aerial vehicle in flight control, it is imperative to design the intelligent controllers that govern flight movements with control rules that are entirely independent of the model. This paper investigates the flight learning performance of an intelligent UAV and the variables that affect this performance. The conclusion drawn from this analysis is that the UAV has the capacity to acquire the ability to fly through the process of machine learning. The integration of learning approaches into flight control has been demonstrated to enhance flight capabilities. It is anticipated that advancements in the utilization of artificial intelligence technology for flight control will give rise to novel sectoral application areas.