Papers
2
Total Citations
12
H-Index
2
About
Pukun Lu is a researcher specializing in adaptive control systems for unmanned aerial vehicles, with a particular focus on quadrotor robotics. Their work addresses critical challenges in trajectory tracking and motor actuation for these autonomous systems. Lu's most cited paper, "Neural Network Based Adaptive Event-Triggered Control for Quadrotor Unmanned Aircraft Robotics" (2022, 7 citations), introduces an innovative control scheme that combines neural networks with event-triggering mechanisms to optimize quadrotor performance while reducing computational load. Building on this foundation, their 2024 study on "Adaptive Observer-Based Implicit Inverse Control" (5 citations) tackles the complex issue of hysteresis in motor-driven quadrotors, validated through real-world experiments on the QDrone platform. This experimental validation distinguishes Lu's work, bridging theoretical control design with practical implementation. By developing adaptive, neural network-driven solutions for quadrotor control, Lu contributes to safer, more efficient autonomous flight systems—advancements with implications for drone delivery, surveillance, and search-and-rescue operations. Their research demonstrates a commitment to solving real-world robotics challenges through intelligent, adaptive control methodologies.
Research Focus
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Top Papers
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