Xiaoke Fang
Papers
6
Total Citations
199
H-Index
4
About
Dr. Xiaoke Fang is a leading researcher in rehabilitation robotics and intelligent control systems, with a primary focus on developing advanced control strategies for robotic exoskeletons used in upper-limb rehabilitation. His most impactful work, "Data-driven model-free adaptive sliding mode control for the multi degree-of-freedom robotic exoskeleton," has garnered 178 citations, establishing a foundational approach for controlling complex robotic systems without requiring precise mathematical models. Dr. Fang has pioneered the application of repetitive control methods to suppress joint position errors in rehabilitation robots, as demonstrated in his 2010 work on single-joint repetitive control. His recent contributions include data-driven model-free adaptive containment control for uncertain rehabilitation exoskeletons with input constraints (2024), addressing real-world challenges of system uncertainty and saturation. Additionally, he has explored adaptive velocity field control and tendon-driven robotic arm control using radial basis function neural networks. Dr. Fang's work bridges theoretical control engineering with practical rehabilitation applications, significantly advancing the safety and effectiveness of robotic therapy for patients with upper-limb disorders.
Research Focus
Key Achievements
Top Papers
- 1
- 2Single-joint repetitive control of upper-limb rehabilitation robot7 citations · 2010
- 3
- 4Adaptive velocity field control of upper-limb rehabilitation robot5 citations · 2016
- 5
- 6