Yunxi Miao
Papers
1
Total Citations
3
H-Index
1
About
Yunxi Miao is a rising researcher in intelligent control systems, with a primary focus on adaptive sliding mode control and reinforcement learning for robotic manipulators. Their most-cited work, "Adaptive sliding mode control of robotic manipulator based on reinforcement learning" (2024), addresses a critical challenge in robotics: the time-varying, nonlinear, and coupled dynamics that degrade tracking performance under traditional control algorithms. By integrating reinforcement learning with sliding mode control, Miao's approach enables parameters to self-adapt in real time, overcoming the limitations of conventional methods that lack adaptive learning capabilities. This contribution is particularly significant for applications requiring high-precision manipulation in uncertain environments. Although early in their career, with the paper currently accumulating 3 citations, the work demonstrates strong potential for impact in the fields of robotics and control theory. Miao's research bridges the gap between classical control theory and modern machine learning, offering a pathway toward more intelligent and autonomous robotic systems. Their work is especially relevant for students and researchers interested in the intersection of adaptive control, reinforcement learning, and robotic manipulation.
Research Focus
Key Achievements
Top Papers
- 1