Xiuchun Xiao
Papers
10
Total Citations
429
H-Index
7
About
Dr. Xiuchun Xiao is a leading researcher in computational intelligence and robotic control, whose work bridges neural dynamics, numerical algorithms, and real-time kinematic systems. His primary research areas include recurrent neural networks (RNNs), dynamic equation solving, and redundancy resolution for robotic manipulators. Dr. Xiao’s most impactful contribution is his pioneering work on solving the time-variant generalized Sylvester equation using RNNs, a breakthrough with applications in robot control and acoustic source localization that has garnered 174 citations. He has also developed generalized repetitive motion planning schemes for redundant robots, integrating dynamic neural networks with nonconvex bound constraints (137 citations), and advanced noise-suppressing Newton algorithms for kinematic control, enhancing robot performance under real-world disturbances. His adaptive gradient neural networks for dynamic linear matrix equations and robust synchronization methods for chaotic systems further demonstrate his versatility. With over 400 total citations across his top papers, Dr. Xiao’s innovations in noise-tolerant, gradient-based neural dynamics have significantly advanced the robustness and efficiency of robotic systems, making his work essential reading for students and researchers in computational robotics and applied mathematics.
Research Focus
Key Achievements
Top Papers
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- 4An Adaptive Gradient Neural Network to Solve Dynamic Linear Matrix Equations27 citations · 2021
- 5Noise-Suppressing Newton Algorithm for Kinematic Control of Robots23 citations · 2019
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