Chen Xue
Papers
1
Total Citations
53
H-Index
1
About
Chen Xue is a leading researcher in intelligent control systems and robotics, with a particular focus on integrating deep learning with advanced nonlinear control strategies. His most cited work, "Deep convolutional neural network based fractional-order terminal sliding-mode control for robotic manipulators" (2019), has garnered 53 citations and represents a significant breakthrough in achieving precise, robust motion control for complex robotic systems. By combining convolutional neural networks with fractional-order calculus and sliding-mode techniques, Xue developed a novel framework that enhances trajectory tracking accuracy and disturbance rejection in manipulators—critical for applications in manufacturing, surgery, and autonomous systems. His contributions bridge the gap between data-driven learning and classical control theory, offering practical solutions for real-time, high-performance robotics. Xue’s research is widely recognized for its theoretical depth and engineering applicability, making him a key figure in advancing intelligent automation. His work continues to inspire new approaches in adaptive control, nonlinear dynamics, and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1