Takahiro Miyoshi

Papers

Total Citations

H-Index

About

Takahiro Miyoshi’s research lies at the intersection of robotics, control systems, and computational intelligence, with a particular focus on neuro-fuzzy approaches for manipulator control. His most notable contribution is the development of a neuro-fuzzy minimum torque change control strategy for direct-drive (DD) manipulators, building on foundational work by Uno et al. (1989). By integrating neural networks and fuzzy logic, Miyoshi optimized robotic trajectories to minimize torque change—a key factor in achieving smooth, energy-efficient motion. His 2005 paper on this topic has garnered 4 citations, reflecting its niche but meaningful impact in the field of robotic control. This work demonstrates his ability to bridge theoretical models with practical implementation, offering a framework that enhances manipulator performance by reducing mechanical stress and improving precision. While his citation count is modest, Miyoshi’s research contributes to the broader goal of intelligent, adaptive robotics, making his work relevant for students and researchers exploring bio-inspired control methods and nonlinear dynamics in robotic systems.