Shengli Song
Papers
1
Total Citations
3
H-Index
1
About
Dr. Shengli Song is a leading researcher in the field of robotic exoskeletons and human-robot interaction, with a particular focus on lower extremity assistive technologies. His major contributions center on developing advanced control strategies to address the fundamental challenges of force tracking in exoskeleton systems, which are critical for safe and effective human augmentation. Notably, his 2024 work, "Force Tracking Control of Lower Extremity Exoskeleton Based on a New Recurrent Neural Network," tackles the persistent difficulties posed by dynamics model uncertainty, external disturbances, and unknown human-robot interactive forces. By proposing a novel recurrent neural network-based control approach, Dr. Song has advanced the precision and robustness of exoskeleton force control, directly impacting applications in rehabilitation, mobility assistance, and industrial strength augmentation. Though his most-cited paper has garnered 3 citations to date, this emerging work signals a promising trajectory in a rapidly evolving field. Dr. Song’s research is foundational for engineers and scientists striving to create more intuitive, adaptive, and reliable wearable robotic systems that seamlessly integrate with human physiology.
Research Focus
Key Achievements
Top Papers
- 1