Young-Doo Kim
Papers
1
Total Citations
2
H-Index
1
About
Young-Doo Kim is a robotics researcher whose work focuses on the control and stabilization of mobile robotic systems, particularly two-wheeled inverted pendulum platforms. His most notable contribution is the development of a neural network-based controller that adapts PID gains in real time to maintain balance under varying load conditions—such as different user weights—addressing a key limitation of fixed-gain controllers. This approach, detailed in his 2011 paper "Controller Design of Two Wheeled Inverted Pendulum Type Mobile Robot Using Neural Network," demonstrates how neural tuning can replace trial-and-error gain selection, improving both stability and robustness. While his citation count (2) reflects a niche but specialized audience, the work is significant for its practical integration of machine learning into classical control for personal mobility robots. Kim’s research bridges neural networks and mechatronics, offering a pathway toward more adaptive and user-friendly robotic transporters. His contributions are particularly relevant for engineers developing self-balancing vehicles and assistive mobility devices.
Research Focus
Key Achievements
Top Papers
- 1