Papers
2
Total Citations
12
H-Index
2
About
Guiying Li is a researcher specializing in advanced control systems for robotic manipulators, with a primary focus on addressing real-world challenges such as unknown payloads and unmodeled dynamics. Her most cited work, an adaptive nonlinear observer-based sliding mode control approach (2020, 10 citations), introduces a novel disturbance observer that estimates external forces from unknown constant payloads, enabling precise robotic handling without prior knowledge of load parameters. This contribution is critical for industrial automation and collaborative robotics. In earlier research (2011), Li tackled the pervasive issue of unmodeled dynamics—nonlinear uncertainties that degrade control performance—by developing a robust adaptive neural network tracking controller. Leveraging radial basis function networks for universal approximation, this work provided a framework for stable operation despite incomplete system models. While her citation counts reflect a focused, emerging impact, Li’s contributions are notable for bridging theoretical control design with practical robotic applications, offering scalable solutions for payload uncertainty and dynamic complexity. Her work is particularly relevant for researchers in adaptive control, nonlinear systems, and intelligent robotics.
Research Focus
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Top Papers
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