Advanced Control Systems for Unmanned Aerial Vehicles (UAVs) Using Neural Networks

Unmanned Aerial Vehicles (UAVs) are becoming increasingly vital in diverse applications such as surveillance, agriculture, and logistics, necessitating the development of advanced control systems to ensure efficient and reliable operations. This paper explores the integration of neural networks in UAV control systems, highlighting contributions from both computer science and aerospace engineering. Neural networks offer robust solutions for handling the complexities and uncertainties inherent in UAV control, enhancing autonomy, adaptability, and performance. Key applications include pattern recognition, autonomous navigation, sensor data fusion, and fault detection. From an aerospace engineering perspective, neural networks address challenges in flight dynamics, adaptive control, robustness to environmental uncertainties, and energy efficiency. Case studies demonstrate the practical benefits of neural network-enhanced UAVs in surveillance, precision agriculture, and disaster response scenarios. This research combines theoretical foundations, practical implementations, and future directions, aiming to advance the state-of-the-art in UAV control systems and contribute to their widespread adoption and effectiveness in various fields.

کلیدواژه ها:

Control Systems ، Aerial Vehicles ، Neural Networks