Xueqian Song

Papers

Total Citations

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About

Xueqian Song is a researcher whose work lies at the intersection of aerial robotics and adaptive control systems, with a particular focus on enabling complex manipulation tasks for quadrotor platforms. His most cited contribution, "Hierarchy-Based Adaptive Generalized Predictive Control for Aerial Grasping of a Quadrotor Manipulator" (2019), has garnered 14 citations, establishing a foundation for dynamic, real-time control in unmanned aerial vehicles (UAVs) equipped with manipulators. Song’s major contribution lies in developing a hierarchical control framework that integrates predictive algorithms with adaptive mechanisms, allowing quadrotors to perform precise aerial grasping despite environmental uncertainties and dynamic disturbances. This work addresses critical challenges in aerial manipulation—such as stability during object interaction and trajectory optimization—paving the way for applications in search-and-rescue, logistics, and infrastructure inspection. While his citation count reflects a growing impact in a specialized field, Song’s approach is notable for bridging theoretical control theory with practical robotic systems, offering a scalable solution for next-generation autonomous drones. His research continues to influence the design of agile, task-capable UAVs, making him a key voice in the advancement of aerial robotics.