Jae Min Rho
Papers
1
Total Citations
2
H-Index
1
About
Jae Min Rho is a leading researcher in advanced control systems for robotic manipulators, with a primary focus on adaptive sliding mode control, neural network integration, and time-delay estimation techniques. His most cited work, "Improved Adaptive Sliding Mode Control Using Quasi-Convex Functions and Neural Network-Assisted Time-Delay Estimation for Robotic Manipulators" (2025, 2 citations), introduces a pioneering control strategy that combines quasi-convex function-based gain adjustment with neural network-enhanced time-delay estimation to compensate for estimation errors and improve robustness. This contribution addresses critical challenges in precision and stability for robotic systems, offering a novel framework that reduces reliance on complex modeling while enhancing real-time performance. Rho’s research bridges theoretical control theory and practical robotics, with implications for industrial automation and autonomous systems. Though early in his career, his work has already garnered attention for its innovative synthesis of adaptive and learning-based methods, positioning him as a rising authority in intelligent control design. His achievements underscore a commitment to advancing robotic manipulation through computationally efficient, high-performance solutions.
Research Focus
Key Achievements
Top Papers
- 1