Moonnoh Lee
Papers
1
Total Citations
4
H-Index
1
About
Dr. Moonnoh Lee is a robotics and control systems researcher whose work focuses on intelligent, adaptive control strategies for autonomous mobile robots. His most cited contribution, "PSO-Based Adaptive Neural Control for Trajectory Tracking of a Mobile Robot" (2020, 4 citations), introduces a novel framework that integrates Particle Swarm Optimization with neural network-based adaptive control. This approach enables a nonholonomic wheeled mobile robot to accurately track desired trajectories despite uncertainties and external disturbances, crucially without requiring precise knowledge of the robot’s kinematic or dynamic parameters. By eliminating the need for exact modeling, Lee’s work advances the practical deployment of robust, model-free controllers in real-world robotic systems. His research sits at the intersection of computational intelligence, adaptive control theory, and mobile robotics, offering scalable solutions for autonomous navigation in uncertain environments. Though early in his citation impact, Lee’s contributions represent a meaningful step toward more resilient and autonomous robotic platforms, with potential applications in industrial automation, service robotics, and field exploration.
Research Focus
Key Achievements
Top Papers
- 1