Lanlan Su

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About

Lanlan Su is a researcher whose work lies at the intersection of adaptive control theory and robotic systems, with a particular focus on enhancing the precision and reliability of robotic manipulators. Her key contributions center on the development of advanced iterative learning control (ILC) strategies that enable robots to improve their trajectory-tracking performance over repeated tasks. In her notable 2024 paper, "Adaptive Iterative Learning Control for Robotic Manipulators," Su introduces a refined control scheme that seamlessly integrates a linear feedback controller with a feedforward learning controller. This dual-controller architecture effectively mitigates unknown disturbances and desensitizes the system to unidentified parameters, addressing two persistent challenges in robotic control. While her work is still gaining traction—with 2 citations to date—its practical implications for manufacturing, automation, and surgical robotics are significant. Su’s approach promises more robust and adaptive robotic systems capable of learning from experience, marking her as an emerging voice in the field of intelligent control systems.