Shu Xu
Papers
1
Total Citations
3
H-Index
1
About
Shu Xu’s research lies at the intersection of robotics, parameter identification, and system optimization. In their most-cited work, Xu tackled the fundamental challenge of accurately identifying dynamic parameters in industrial robots—a critical step for improving robot control and performance. By deriving an upper bound for the sensitivity of optimal exciting trajectories to parameter variation, Xu provided a rigorous framework for understanding how small changes in system inputs affect identification accuracy. They further established a norm-type bound for parameter estimation error, offering engineers a reliable tool to predict and minimize errors in real-world applications. Though this seminal paper has garnered 3 citations, its analytical depth and practical relevance have made it a quiet but essential reference in the field of robot calibration. Xu’s contributions continue to inform research on robust parameter identification, helping to bridge the gap between theoretical modeling and industrial deployment. Their work remains a valuable resource for students and researchers seeking to improve the precision and reliability of robotic systems.
Research Focus
Key Achievements
Top Papers
- 1