An Luo
Papers
2
Total Citations
17
H-Index
2
About
An Luo has made foundational contributions to the field of iterative learning control (ILC), particularly for uncertain robotic systems. Her research focuses on developing adaptive and robust control strategies that enable robots to improve performance through repeated task execution, even in the presence of model uncertainties. In her most-cited work (2003, 12 citations), Luo introduced an adaptive robust ILC scheme that decomposes system uncertainty into repetitive and non-repetitive components, using Lyapunov methods to guarantee stability and convergence—a significant advancement for real-world robotic applications. Her 2004 paper (5 citations) further advanced the field by addressing the critical role of initial control input values in ILC convergence and stability. Luo proposed novel experience-based methods—including linear weighted averaging and cumulative approaches—for intelligently acquiring these initial values from past control data, thereby enhancing learning efficiency and system reliability. While her citation counts reflect a focused, specialized impact, her work has been instrumental in bridging theoretical ILC frameworks with practical robotic control challenges, influencing subsequent research in adaptive learning systems and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1Adaptive robust iterative learning control for uncertain robotic systems12 citations · 2003
- 2