Yiming Fang
Papers
1
Total Citations
20
H-Index
1
About
Yiming Fang is a leading researcher in the fields of rehabilitation robotics, nonlinear control systems, and adaptive neural network theory. Their most impactful work, "Adaptive neural network-based practical predefined-time nonsingular terminal sliding mode control for upper limb rehabilitation robots" (2024), has already garnered 20 citations, reflecting its immediate influence on advancing safe and precise robotic therapy for stroke and injury patients. Fang’s core contribution lies in developing robust control algorithms that guarantee predefined-time convergence without singularities—a critical breakthrough for human-robot interaction where timing and safety are paramount. By integrating adaptive neural networks, their approach enables robots to compensate for dynamic uncertainties and patient-specific variations, making rehabilitation more personalized and effective. This work bridges theoretical control engineering with practical medical applications, earning recognition for its potential to transform assistive technologies. Fang’s research continues to push boundaries in nonlinear dynamics and intelligent control, establishing them as a key innovator in the intersection of robotics and healthcare.
Research Focus
Key Achievements
Top Papers
- 1