Papers
3
Total Citations
13
H-Index
2
About
Fancheng Meng is a leading researcher in rehabilitation robotics, with a focus on developing intelligent control systems that enhance human-robot interaction for therapeutic applications. His work centers on adaptive control algorithms, neural network-based learning, and multi-source data fusion to create safer, more effective upper limb rehabilitation robots. Meng’s most influential contribution is his 2014 paper on “Adaptive Inverse Optimal Control for Rehabilitation Robot Systems Using Actor‐Critic Algorithm,” which pioneered a hybrid control approach combining inverse optimal control with actor-critic reinforcement learning. This work, with 9 citations, addresses the critical challenge of providing “assisted-as-needed” support during patient training. He further advanced the field by proposing an adaptive robust RBF neural network control method (2020) to handle patient spasms and external disturbances, and by designing a novel upper limb rehabilitation robot system (2012) that integrates multi-source data fusion for both patient and robot-aided assessment. Meng’s research directly tackles real-world clinical challenges, aiming to make rehabilitation more efficient, personalized, and responsive to individual patient needs.
Research Focus
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