Mingxuan
Papers
1
Total Citations
6
H-Index
1
About
Mingxuan is a leading researcher in the field of advanced control theory, with a primary focus on repetitive learning control for time-varying robotic systems. Their most cited work, "Repetitive Learning Control for Time-varying Robotic Systems: A Hybrid Learning Scheme" (2007, 6 citations), introduces a groundbreaking hybrid learning approach that addresses the challenge of tracking finite-time trajectories in robotic systems with uncertain, time-varying dynamics. A key innovation is the elimination of the need for initial repositioning at the start of each cycle, a common limitation in conventional repetitive control methods. By employing a hybrid learning scheme that estimates both periodic and non-periodic time-varying unknowns without relying on Taylor series expansions, Mingxuan ensures that system state variables remain bounded and tracking errors converge to zero as iterations increase. This work has significant implications for precision robotics and adaptive systems, offering a robust framework for real-world applications. With 6 citations, this foundational paper continues to influence researchers in control engineering and robotics, highlighting Mingxuan's contributions to advancing learning-based control methodologies.
Research Focus
Key Achievements
Top Papers
- 1