Ming Chen
Papers
1
Total Citations
5
H-Index
1
About
Ming Chen is an emerging researcher whose work sits at the intersection of control theory, robotics, and rehabilitation engineering. His most recognized contribution to date is his 2024 paper on Model Predictive Control (MPC) for attitude tracking in lower-limb rehabilitation exoskeleton robots, which has already garnered 5 citations within a short period — a promising indicator of growing influence in the field. In this work, Chen tackled a critical challenge in assistive robotics: designing robust controllers capable of handling real-world complexities such as parameter uncertainties and external disturbances. His innovative approach involved reformulating the exoskeleton dynamics into a fully-actuated system model before applying MPC, offering a principled and computationally tractable framework for precise attitude tracking. This contribution is particularly meaningful given the growing global demand for effective rehabilitation technologies for patients with mobility impairments. By bridging advanced control strategies with practical medical robotics applications, Chen's research holds significant potential to improve the performance and reliability of next-generation exoskeleton systems. Students and researchers working in adaptive control, human-robot interaction, or biomedical engineering will find his work both technically rigorous and clinically relevant.
Research Focus
Key Achievements
Top Papers
- 1