Chenchen Xu

Nanjing University of Science and Technology

Papers

Total Citations

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About

Chenchen Xu is a researcher whose work lies at the intersection of robotics, adaptive control, and neural network applications. Their primary research focus is on achieving high-accuracy motion control for robotic manipulators, particularly in the presence of model uncertainties—a persistent challenge in real-world automation. Xu’s most-cited paper, "High accuracy adaptive motion control for a robotic manipulator with model uncertainties based on multilayer neural network" (2021, 18 citations), introduces a novel approach that leverages multilayer neural networks to compensate for both structural and unstructured uncertainties in robotic systems. This work addresses a critical gap in traditional control methods, offering a robust solution that enhances precision and stability. By integrating adaptive control with neural network learning, Xu has contributed to safer, more reliable robotic operations in complex environments. Their research holds significant implications for industrial automation, where manipulators must perform delicate tasks despite unpredictable dynamics. With growing recognition in the control systems community, Chenchen Xu continues to push the boundaries of intelligent robotic motion, making their work essential reading for students and researchers interested in advanced robotics and adaptive control theory.