Yichun Yan
Papers
1
Total Citations
2
H-Index
1
About
Yichun Yan is a researcher focused on the intersection of robotics, control systems, and neural network applications, with a particular emphasis on improving the precision and dynamic performance of robotic manipulators. Their most cited work, "Neural network compensation of dynamic errors in a position control system of a robot manipulator" (2020), addresses a critical challenge in robotics: mitigating dynamic errors that degrade positioning accuracy. In this paper, Yan reviews existing approaches to nonlinear error compensation and presents a mathematical framework for integrating neural networks into the control architecture of a 3-link robot manipulator, offering a pathway toward more adaptive and intelligent robotic systems. While their citation count is currently modest, this foundational work signals a promising trajectory in the field of intelligent control. Yan’s contributions are particularly relevant for researchers and students exploring how machine learning can enhance traditional control methods, bridging the gap between theoretical control theory and practical robotic applications. Their work underscores a growing trend toward data-driven solutions in precision engineering, positioning them as an emerging voice in the advancement of next-generation robotic systems.
Research Focus
Key Achievements
Top Papers
- 1