Papers

9

Total Citations

198

H-Index

6

About

Dr. Zhenlei Chen is a leading researcher in the field of rehabilitation robotics and human-robot interaction, with a primary focus on lower limb exoskeleton systems. His work centers on developing intelligent control strategies that enable exoskeletons to adaptively assist human movement, addressing critical challenges in model identification, human-robot coupling control, and compliant interaction. Dr. Chen’s most influential contribution is his "Flexible assistance strategy of lower limb rehabilitation exoskeleton based on admittance model" (2024, 59 citations), which introduces an innovative approach to personalized robotic assistance. He has also made significant advances in adaptive control using bio-inspired optimization algorithms, as demonstrated in his highly cited work on "Model identification and adaptive control of lower limb exoskeleton based on neighborhood field optimization" (2021, 41 citations). With over 200 total citations across his publications, Dr. Chen has established himself as an authority in exoskeleton control theory. His research portfolio extends to distributed impedance control for networked robotic systems and fault-tolerant control for manipulators, showcasing his versatility in addressing complex robotic challenges. Dr. Chen’s work is particularly notable for its practical implementation, including the development of a 2-DOF lower limb exoskeleton experimental platform that serves as a testbed for evaluating human-robot interaction dynamics.

Research Focus

Key Achievements

6
H-Index
9
Papers
198
Total Citations
22
Avg Citations/Paper
🏆 Most Cited Paper
Flexible assistance strategy of lower limb rehabilitation exoskeleton based on admittance model
59 citations · 2024
📈 Most Prolific Year: 2024 (4 Papers)
🤝 Key Collaborators: 21
🏛 Institutions: University of Electronic Science and Technology of China, Zhejiang A & F University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago