Research on UAV Intelligent Control Model Based on Deep Learning and Reinforcement Learning

With the rapid development of UAV technology, intelligent control systems have become an important factor in improving their stability, efficiency and adaptability. Traditional flight control methods have limitations when dealing with complex environments and dynamic tasks, while intelligent control models based on artificial intelligence can achieve more efficient and autonomous flight control by combining technologies such as deep learning, reinforcement learning and fuzzy control. This paper first reviews the current research status of UAV intelligent control models and analyzes the application advantages of methods such as deep learning and reinforcement learning in flight attitude control, path planning and target recognition. Then, an intelligent framework combining multi-task learning and adaptive control is proposed to improve the decision-making ability of UAVs in dynamic environments. Through actual case analysis, this paper demonstrates the application results of intelligent control in tasks such as autonomous navigation, obstacle avoidance, and cluster collaboration, and points out the challenges in technical application, such as real-time requirements and computational complexity. Finally, this paper looks forward to the future development direction of UAV intelligent control technology, discusses the potential of emerging technologies such as edge computing and multi-sensor fusion in improving control accuracy and response speed, and puts forward corresponding technical optimization suggestions, providing theoretical and technical support for the further intelligence of UAV systems.