Han-Go Choi
Papers
1
Total Citations
6
H-Index
1
About
Han-Go Choi is a researcher whose work lies at the intersection of nonlinear dynamics, intelligent control, and robotics. His most-cited paper, "Trajectory control of robotic manipulators using chaotic neural networks" (2002, 6 citations), introduces a novel approach to direct adaptive control by leveraging the unique properties of chaotic neural networks. Unlike conventional neural networks, these networks incorporate self and internal feedback loops, enabling them to exhibit robust performance when controlling highly nonlinear dynamic systems. This contribution is particularly significant for the precise trajectory control of robotic manipulators, where stability and adaptability are critical. While his citation count reflects a focused and specialized impact, Choi’s work demonstrates a forward-thinking application of chaos theory to practical engineering challenges. His research offers valuable insights for students and researchers exploring adaptive control, neural network architectures, and the integration of chaotic dynamics into real-world robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Trajectory control of robotic manipulators using chaotic neural networks6 citations · 2002