Guirong Liu
Papers
3
Total Citations
142
H-Index
3
About
Guirong Liu is a leading researcher in robotics and intelligent control systems, with a primary focus on the modeling, identification, and precision control of complex robotic manipulators. His most notable contribution is the development of the TubeNet—a novel two-way neural network architecture that enables real-time, inverse identification of critical mechanical parameters. In his highly cited 2008 work (124 citations), Liu introduced a cascade sliding-mode control strategy for hydraulically driven 6-DOF parallel robots, establishing a robust framework for high-precision motion control under nonlinear dynamics. Building on this foundation, his 2021 studies demonstrate a systematic approach for inversely identifying joint stiffnesses and other uncertain parameters in robot arms, directly addressing the challenge of maintaining accurate tip posture and movement. These methods allow for quantitative, real-time parameter estimation without requiring disassembly or complex sensor arrays. Liu’s work bridges the gap between theoretical control design and practical robotic applications, offering engineers powerful tools for calibration and adaptive control. His research is particularly impactful for advanced manufacturing, surgical robotics, and any field demanding high-fidelity robotic arm performance.
Research Focus
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Top Papers
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