Gendi Liu
Papers
14
Total Citations
251
H-Index
8
About
Gendi Liu is a robotics and control systems researcher whose work centers on the design and implementation of advanced control strategies for soft robotic systems, particularly those driven by pneumatic artificial muscles (PAMs). His research addresses one of the most pressing challenges in intelligent robotics: taming the complex nonlinearities, hysteresis, and input uncertainties inherent in PAM actuators to achieve precise, reliable motion control. Liu has made substantial contributions through the development of hybrid control frameworks that fuse fuzzy logic, sliding mode control, neural networks, reinforcement learning, and adaptive compensation techniques — approaches that have significantly advanced the performance of humanoid manipulators, exoskeletons, and parallel rehabilitation robots. His most influential work, a 2021 study on fuzzy-sliding mode control for humanoid arm robots, has accumulated 66 citations, reflecting its broad impact on the field. Subsequent contributions exploring reinforcement learning-based prescribed performance control and neural network-driven adaptive filtering have further cemented his reputation, collectively drawing over 200 citations across his body of work. Liu's research has meaningful real-world implications for rehabilitation engineering and human-robot interaction, where compliant, safe, and responsive actuation is critical. His consistent output across multiple high-impact publications demonstrates a disciplined and evolving research program pushing the boundaries of intelligent soft robotic control.
Research Focus
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