Songguo Liu
Papers
9
Total Citations
66
H-Index
6
About
Songguo Liu is a robotics researcher whose work centers on robot kinematics, motion control, and intelligent trajectory planning for industrial and humanoid robotic systems. His most significant contributions lie in solving the inverse kinematics problem for general 6R robots — a computationally demanding challenge critical to real-time robot control. His 2007 Matlab-based simulation study of the QJ-6R welding robot (14 citations) established a foundation for practical kinematic analysis, while subsequent papers introduced optimized, real-time inverse kinematics algorithms exploiting the orthogonal properties of rotation sub-matrices, streamlining computation for online control applications. Beyond kinematics, Liu has made notable contributions to robust control design. His adaptive PID controller with H∞ tracking performance addresses nonlinear uncertainties and disturbances in robot manipulators, and his robust adaptive wavelet network controller demonstrates a forward-looking integration of neural computation with classical control theory. His 2009 work on B-spline-based joint trajectory planning for humanoid robots reflects a commitment to smooth, stable locomotion. Later research expanded into dynamic modeling using screw theory combined with sliding mode control. Across his portfolio, Liu's work has accumulated over 60 citations, reflecting meaningful influence on robotics engineering and control systems research.
Research Focus
Key Achievements
Top Papers
- 1Kinematics analysis and simulation of QJ-6R welding robot based on Matlab14 citations · 2007
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- 6Smooth joint trajectory planning for humanoid robots based on B-splines6 citations · 2009
- 7Adaptive PID control of robot manipulators with H_∞ tracking performance5 citations · 2009
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- 9Robust Adaptive Wavelet Network Control for Robot Manipulators3 citations · 2009