Satoka Hiraoka
Papers
1
Total Citations
7
H-Index
1
About
Dr. Satoka Hiraoka’s research lies at the intersection of advanced neural network architectures and robotic control systems, with a particular focus on leveraging quaternion mathematics to enhance manipulator performance. Her most cited work, “Remarks on Control of a Robot Manipulator using a Quaternion Recurrent Neural-Network-Based Compensator” (2020, 7 citations), introduces a novel recurrent quaternion neural network designed to compensate for nonlinearities and uncertainties in robotic arm trajectory tracking. By integrating computed torque control with quaternion-based representations, Hiraoka demonstrates how this approach effectively minimizes end-effector positioning errors, offering a more robust and computationally efficient alternative to traditional real-valued networks. This contribution is especially significant for applications requiring precise spatial orientation, such as industrial automation and surgical robotics. While her citation count reflects the emerging nature of this specialized field, Hiraoka’s work is pioneering in bridging quaternion algebra with recurrent neural dynamics, providing a foundation for future research in intelligent robotic control. Her innovative methodology underscores a commitment to solving real-world control challenges through mathematically rigorous and practically implementable solutions.
Research Focus
Key Achievements
Top Papers
- 1