Papers
11
Total Citations
166
H-Index
7
About
Jiapeng Liu is a dynamic researcher whose work sits at the intersection of intelligent control systems, robotics, and human-robot interaction. His research primarily focuses on adaptive control theory for robotic manipulators, with particular expertise in finite-time and fixed-time control methods, fuzzy and neural network-based approximation, command-filtered backstepping, and constraint satisfaction using barrier Lyapunov functions. Liu's most influential contribution, "Adaptive Neural Network-Based Finite-Time Impedance Control of Constrained Robotic Manipulators With Disturbance Observer" (2021, 51 citations), exemplifies his hallmark approach: elegantly combining multiple advanced control techniques to achieve faster convergence, robustness against disturbances, and guaranteed safety constraints. His parallel work on fuzzy finite-time impedance control for physical human-robot interaction (23 citations) further demonstrates his commitment to making robots safer and more compliant in real-world applications. Beyond theoretical contributions, Liu has tackled practical challenges including fault-tolerant control, stochastic systems with dead-zone inputs, flexible-joint robot dynamics, and even RGB-D vision-guided robotic docking systems. His accumulated citations across a focused body of work reflect meaningful influence within the robotics and control engineering community. Researchers studying safe, adaptive, and intelligent robotic control will find Liu's publications an essential and richly detailed reference.
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