Huanlin Li
Papers
1
Total Citations
2
H-Index
1
About
Huanlin Li is a leading researcher in robotics and dynamic system identification, with a focus on developing high-fidelity models for robotic manipulators. Their major contribution lies in advancing parameter estimation techniques that integrate complex friction models, such as the LuGre model, into real-world robotic control. In their highly cited 2024 work, Li proposed a novel two-stage identification framework that first extracts link parameters—mass, length, and inertia—from excitation trajectories, then refines friction coefficients using an Improved Grey Wolf Optimizer. This approach significantly enhances torque prediction accuracy, addressing a critical challenge in precision robotics. Although early in its publication cycle, the paper has already garnered attention for its practical impact on robotic calibration and control. Li’s work is notable for bridging theoretical optimization algorithms with experimental validation, offering a robust methodology for engineers seeking to improve manipulator performance. Their research continues to influence the fields of mechatronics and adaptive control, making them a rising voice in the development of smarter, more responsive robotic systems.
Research Focus
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Top Papers
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