Papers
5
Total Citations
76
H-Index
3
About
Byeonggi Yu is a robotics researcher whose work bridges adaptive control, kinematic modeling, and neurorehabilitation. His primary contributions lie in developing robust control strategies for robot manipulators under challenging real-world conditions. His most impactful work, "An Adaptive PID Control for Robot Manipulators Under Substantial Payload Variations" (2020, 58 citations), addresses a critical practical problem: maintaining stable control when robotic arms handle unpredictable loads. Yu introduced an adaptive gain mechanism that outperforms previous methods in both stability and ease of implementation, offering a practical solution for manufacturing and logistics. He further refined this approach in "An Effective Adaptive Gain Dynamics for Time-Delay Control of Robot Manipulators" (2020), enhancing the robustness of model-free time-delay control—a technique valued for its simplicity and efficiency. Beyond control theory, Yu has advanced robot kinematics by developing universal methods to convert Unified Robot Description Format (URDF) files into Denavit–Hartenberg (DH) parameters, a fundamental step for simulation and motion planning. His interdisciplinary work extends to neuroscience, where he explored entropy as a metric for quantifying brain activation during physical tasks using functional near-infrared spectroscopy (fNIRS). With a growing citation record and contributions spanning theory to application, Yu is establishing himself as a versatile researcher shaping the future of adaptive robotics and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5