Papers
20
Total Citations
407
H-Index
12
About
Jinpeng Yu is a prolific researcher specializing in advanced control systems for robotic manipulators, with particular expertise in adaptive fuzzy neural network control, impedance control, and constrained nonlinear systems. His work consistently addresses critical challenges in robotics, including system uncertainties, full-state constraints, and disturbance rejection, making substantial contributions to both theoretical foundations and practical applications. Yu's most influential contributions center on integrating adaptive fuzzy neural networks with impedance control frameworks for constrained robotic manipulators. His 2021 papers on command filtered impedance control — garnering 73 and 51 citations respectively — introduced barrier Lyapunov functions and finite-time stability theory to achieve safer, more responsive human-robot interaction. His research extends to fault-tolerant control, flexible-joint robots, and agricultural harvesting robots employing uncalibrated visual servoing with RGB-D cameras, demonstrating impressive breadth across both theoretical and applied domains. With over 300 cumulative citations across his top works, Yu has established a meaningful presence in the robotics control community. His ongoing investigations into fixed-time fuzzy adaptive control and transient performance reflect a commitment to pushing convergence speed and robustness boundaries. Researchers working in adaptive control, physical human-robot interaction, or constrained nonlinear systems will find Yu's body of work an essential reference point.
Research Focus
Key Achievements
Top Papers
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- 8Survey of transient performance control20 citations · 2023
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