Kenichi Hamamoto
Papers
2
Total Citations
59
H-Index
2
About
Kenichi Hamamoto is a leading figure in the field of robotic control systems, with a primary research focus on iterative learning control (ILC) for robot manipulators. His most significant contribution is the development of a novel ILC algorithm that operates within a finite-dimensional input subspace, enabling perfect tracking for uncertain manipulators without relying on time derivatives of tracking error signals. This work, published in 2002 and cited over 50 times, addresses a critical challenge in robotics—achieving high-precision motion in the presence of system uncertainties. Hamamoto’s approach simplifies control design while enhancing robustness, making it highly applicable to industrial and service robotics. His 2003 follow-up paper further refines the convergence conditions of the learning law, solidifying the theoretical foundation of his method. With a combined citation count exceeding 60 for these core papers, Hamamoto’s research has influenced subsequent work in adaptive and learning-based control. His achievements demonstrate a deep understanding of both theoretical control theory and practical robotic applications, offering a powerful tool for engineers seeking to improve manipulator performance in repetitive tasks.
Research Focus
Key Achievements
Top Papers
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