A Comprehensive Review on Artificial Intelligence-Based Control Strategies for Drone BLDC Motors

The application of brushless direct current (BLDC) motors in Unmanned Aerial Vehicles (UAVs) has revolutionized modern uses of drones by enabling greater levels of performance efficiency, light weighting, and high torque-to-weight ratio that are particularly valuable for aerial systems. The motors also enable high speed at low maintenance, which is particularly valuable for propulsion in UAVs. But the UAV control in real situations usually faces difficulties due to parameter changes, unstructured environments, external disturbances like wind, and system nonlinearities inherent to the system. In such situations, conventional control methods like PID controllers and linear feedback approaches, although useful in structured and stable environments, tend to lack robustness and adaptability for dynamic and uncertain environments. To overcome these deficiencies, artificial intelligence (AI)-based control methods have appeared as revolutionary solutions. Methods such as fuzzy logic controllers, artificial neural networks, reinforcement learning methods, and hybrid intelligent control architectures are increasingly being utilized in UAV flight control and propulsion. Such techniques have the ability to learn and adapt to uncertainties, provide real-time optimal performance, and enhance stability and energy efficiency. This review entails a comprehensive comparison between conventional control approaches and AI-based methods, focusing on their strengths and limitations in the context of UAVs. It also outlines recent advancements in AI-aided optimization, intelligent fault detection strategies, and AI-based real-time trajectory planning in BLDC motor-powered UAVs. Moreover, the paper proposes significant research gaps and technological challenges such as making digital twin integration for UAVs safe, applying onboard edge AI solutions to accelerate processing, and creating advanced sensor less AI controllers for low-cost but precise motor control. Through its emphasis on these fields, the study highlights the contribution of AI in building next-generation UAV systems that are accurate, fault-free, and highly energy-efficient, ushering in more UAV expansion in defense, logistics, agriculture, and health industries across the world.