Minfeng Zhu
Papers
1
Total Citations
18
H-Index
1
About
Minfeng Zhu is a researcher in robotics and control systems, with a primary focus on advanced trajectory tracking and iterative learning control (ILC) for robotic manipulators. Their most-cited work, "Estimation-Based Quadratic Iterative Learning Control for Trajectory Tracking of Robotic Manipulator With Uncertain Parameters" (2020, 18 citations), introduces an improved quadratic-criterion-based ILC approach that significantly enhances tracking performance under parameter uncertainty. This contribution addresses a critical challenge in precision robotics—maintaining accuracy when system dynamics are unknown or variable. Zhu’s research bridges theoretical control design and practical robotic applications, offering robust solutions for industrial manipulators operating in uncertain environments. Their work has been recognized for its potential to improve automation reliability, with citations reflecting its relevance to both control theory and robotics engineering. By developing estimation-based methods that optimize learning over repeated tasks, Zhu contributes to the broader goal of making robotic systems more adaptive and precise. Their achievements underscore a commitment to advancing intelligent automation, with implications for manufacturing, surgical robotics, and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1