Intelligent Control Techniques for Unmanned Aerial Vehicles

Dear Colleagues,

Unmanned Aerial Vehicles (UAVs), commonly known as drones, have rapidly evolved from niche tools to essential assets across a broad range of industries. UAVs are now integral to fields such as agriculture, surveillance, search and rescue, logistics, and environmental monitoring. A key factor driving their effectiveness is the adoption of intelligent control techniques, which enable UAVs to operate autonomously, adapt to dynamic environments, and perform complex tasks with high precision. As UAVs are often deployed in unpredictable or hazardous environments, traditional control methods are frequently insufficient to ensure reliability and safety. This is where intelligent control technologies, such as adaptive control, fuzzy logic, reinforcement learning, and neural networks, come into play. These methods allow for the UAVs to learn from their surroundings, make real-time decisions, and adjust their behaviors without human intervention, leading to improved mission success rates and operational efficiency.

The objective of this Special Issue is to explore the latest advancements in intelligent control techniques for UAVs, with an emphasis on algorithms, architectures, and applications that enhance the autonomy, reliability, and robustness of UAV systems. This Special Issue invites original research articles that contribute significantly to theoretical, numerical, and experimental developments in UAV control, as well as application-driven innovations. Review articles highlighting the state-of-the-art in UAV intelligent control are also encouraged.

Potential topics include, but are not limited to, the following:

• Adaptive control techniques for UAVs;
• Fuzzy logic and hybrid control systems;
• Reinforcement learning and deep learning for autonomous UAVs;
• Path planning and trajectory optimization;
• Real-time decision-making algorithms;
• Robust control for uncertain and dynamic environments;
• UAV swarm intelligence and multi-agent systems;
• Fault detection and diagnosis in UAV systems;
• Sensor fusion and perception for autonomous flight;
• Vision-based control for obstacle avoidance and navigation;
• Safety and reliability analysis in UAV systems;
• Autonomous UAVs for precision agriculture, disaster response, and surveillance;
• Human–UAV interactions and collaborative systems;
• 5G and IoT integration for UAV control systems;
• UAV control in GPS-denied environments;
• Advanced simulation models for UAV control and testing.

Dr. Maolong Lv
Dr. Junkai Ren
Prof. Dr. Haibin Duan
Guest Editors

Manuscript Submission Information

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• unmanned aerial vehicles
• intelligent control
• stability analysis
• adaptive learning control