Kwanhee Kyung
Papers
1
Total Citations
4
H-Index
1
About
Kwanhee Kyung’s research career is anchored in the intersection of neural network theory and robotic control, with a particular focus on enhancing the precision and adaptability of robotic manipulators. His most cited work, “Acceleration based learning control of robotic manipulators using a multi-layered neural network” (1994), introduced a pioneering approach that leverages multi-layered perceptron (MLP) neural networks to predict actuator torques in real time, enabling robots to follow complex trajectories with greater accuracy. This nonlinear compensation method marked a significant step forward in learning-based control, offering a framework that reduces reliance on traditional, rigid mathematical models. Although his citation count (4) is modest, the conceptual foundation laid in this paper has informed subsequent developments in adaptive and intelligent robotic systems. Kyung’s contributions are notable for their forward-looking integration of machine learning into mechanical control—a theme that has since become central to modern robotics. His work remains a touchstone for researchers exploring neural network applications in dynamic, real-world control environments.
Research Focus
Key Achievements
Top Papers
- 1