Papers
2
Total Citations
4
H-Index
1
About
Yanli Feng is a robotics researcher focused on advancing the dynamic modeling and control of robotic manipulators. Their work addresses critical challenges in robot precision, particularly in compensating for nonlinear friction and developing adaptive control strategies under uncertainty. Feng’s most cited paper, “A nonlinear robot joint friction compensation method including stick and sliding characteristics” (2023, 3 citations), introduces a novel approach to improve the accuracy of dynamic models for n-DOF serial robots by explicitly modeling stick-slip friction behaviors—a key factor limiting performance in high-precision tasks. Building on this, Feng’s 2024 paper on output feedback control for uncertain robot manipulators leverages reinforcement learning to enable adaptive control without full state measurement, pushing toward more autonomous and robust systems. Though early in their career, Feng’s contributions are already shaping practical solutions for industrial and service robotics, with their work cited in emerging research on model-based control and learning-driven automation. Their focus on bridging theoretical modeling with real-world friction dynamics positions them as a promising voice in modern robot control.
Research Focus
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Top Papers
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