Hengyi Li

Papers

Total Citations

H-Index

About

Hengyi Li is a researcher at the forefront of rehabilitation robotics and adaptive control systems, with a particular focus on humanoid gait analysis and lower limb assistive technologies. His most-cited work, "RBF Network-Based Adaptive Control for Humanoid Gait Data" (2025), addresses a critical challenge in rehabilitation robotics: balancing model uncertainty compensation with vibration suppression in sliding mode control. Li’s major contribution lies in proposing an adaptive control strategy that integrates radial basis function (RBF) neural networks, enabling more stable and responsive control of lower limb rehabilitation robots. This work has garnered early attention with 1 citation, signaling its growing relevance in the field. Li’s research bridges neural network theory and practical robotic control, offering promising solutions for improving the safety and effectiveness of gait rehabilitation devices. His achievements highlight a commitment to advancing human-robot interaction and assistive technology, making his work essential reading for researchers in adaptive control, biomechatronics, and rehabilitation engineering.