Masumi Ueno
Papers
1
Total Citations
6
H-Index
1
About
Masumi Ueno is a researcher whose work lies at the intersection of adaptive control systems and artificial neural networks, with a particular focus on iterative learning control (ILC) for trajectory tracking. Their most-cited paper, "Trajectory tracking control by an adaptive iterative learning control with artificial neural networks" (2001), addresses a fundamental limitation of conventional ILC algorithms—the requirement for nominal system parameters. By integrating adaptive mechanisms with neural networks, Ueno proposed a more flexible and robust control framework capable of achieving perfect trajectory tracking without prior knowledge of system dynamics. This contribution has been recognized with 6 citations, establishing a foundation for further advances in intelligent control. Ueno’s research is particularly relevant for applications in robotics, precision manufacturing, and autonomous systems, where accurate and adaptive motion control is essential. Their work exemplifies a thoughtful synthesis of classical control theory and modern machine learning, offering practical solutions to long-standing engineering challenges. For students and researchers exploring adaptive control or neural-network-based systems, Ueno’s contributions provide a clear and impactful starting point.
Research Focus
Key Achievements
Top Papers
- 1