Hidemi Yamasaki
Papers
1
Total Citations
7
H-Index
1
About
Dr. Hidemi Yamasaki is a researcher specializing in robotics, neural network control, and nonlinear dynamics, with a particular focus on advanced computational methods for manipulator systems. Their most notable contribution is the development of a quaternion recurrent neural network-based compensator for robot manipulator control, as detailed in their 2020 paper “Remarks on Control of a Robot Manipulator using a Quaternion Recurrent Neural-Network-Based Compensator.” This work integrates quaternion algebra—which efficiently represents three-dimensional rotations—with recurrent neural networks to enhance trajectory tracking accuracy. By employing computed torque control alongside the neural compensator, Yamasaki demonstrated a robust method for correcting end-effector positioning errors, addressing key challenges in real-time robotic control. While their citation count (7 for this paper) reflects a focused, emerging impact, the work stands out for its innovative fusion of quaternion theory and adaptive learning, offering a promising pathway for more precise and stable robotic manipulation in industrial and service applications. This contribution underscores Yamasaki’s commitment to bridging theoretical neural network research with practical engineering solutions, marking them as a thoughtful contributor to the field of intelligent robotics.
Research Focus
Key Achievements
Top Papers
- 1