Papers
39
Total Citations
964
H-Index
20
About
Qingcong Wu is a prominent researcher specializing in rehabilitation robotics, exoskeleton control systems, and human-robot interaction, with a particular focus on restoring motor function in individuals with neuromuscular impairments. His work has made substantial contributions to the development of intelligent control strategies for upper and lower limb rehabilitation exoskeletons, accumulating over 580 citations across his most influential publications. Wu's most celebrated contributions include pioneering minimal-intervention-based admittance control strategies and neural-fuzzy adaptive control frameworks that enable rehabilitation robots to respond dynamically to patient effort and intent. His 2017 paper on admittance control (108 citations) and his 2018 RBFN-based neural-fuzzy approach (97 citations) established foundational methodologies widely adopted in the field. He has also advanced patient-active control paradigms that meaningfully integrate the user's voluntary participation into therapy, a factor clinical evidence suggests is critical for recovery outcomes. More recently, Wu has pushed the frontier of soft and variable-stiffness exoskeleton design, leveraging surface electromyography (sEMG) signals to decode user intention and drive adaptive torque estimation. His interdisciplinary approach — bridging biomechanics, artificial muscle modeling, and fuzzy sliding mode control — reflects a comprehensive vision for the next generation of compliant, intelligent rehabilitation devices that are both effective and human-centered.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10