Xiaoping Ye
Papers
8
Total Citations
129
H-Index
6
About
Xiaoping Ye is a researcher whose work spans robotics control, intelligent systems, and mechatronic design, with particular expertise in space robotics and adaptive neural network control. Over the course of his career, Ye has made significant contributions to solving one of space robotics' most persistent challenges: achieving precise trajectory tracking and vibration suppression in free-floating robotic manipulators operating under conditions of uncertainty and limited sensor feedback. His most influential work, garnering 50 citations, introduced output feedback control for space robotic manipulators using adaptive fuzzy neural network techniques, establishing a foundational approach that later informed his 2022 research on vibration suppression in flexible-jointed robots (34 citations). Across multiple studies, Ye has pioneered the use of RBF neural networks and variable structure control to handle the complex, coupled dynamics inherent in both space and industrial robotic systems. Beyond robotics, Ye has contributed meaningfully to mechatronic systems design methodology, proposing novel SysML-based geometry profiles to streamline multidisciplinary collaborative design workflows and formal functional modeling approaches for software-physical integration. His research collectively reflects a commitment to bridging intelligent control theory with practical engineering design, making his work valuable to students and researchers working at the intersection of robotics, control systems, and mechatronics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4Variable Structure Control for Space Robots Based on Neural Networks9 citations · 2014
- 5Robust Control for Robotic Manipulators Base on Adaptive Neural Network6 citations · 2014
- 6
- 7
- 8