Masayuki Katagiri
Papers
1
Total Citations
7
H-Index
1
About
Masayuki Katagiri is a researcher whose work lies at the intersection of robotics, intelligent control systems, and computational intelligence. His primary research areas include force and position control of robot manipulators, neurocontrollers, and genetic algorithm (GA) based training methods. Katagiri’s major contribution is the development of a novel controller that integrates neural networks with genetic algorithms to simultaneously manage both force and position control—a notoriously challenging problem in robotics. His most cited paper, “Force and position control of robot manipulator using neurocontroller with GA based training” (2004), has garnered 7 citations, demonstrating its foundational role in advancing adaptive control strategies for robotic systems. By employing a simple three-layer neural network trained via GA, Katagiri’s work offers a practical solution to the complex task of hybrid force-position control, paving the way for more versatile and autonomous robot manipulators. His research is particularly notable for its application-oriented approach, bridging theoretical control methods with real-world robotic challenges. Katagiri’s contributions continue to influence the fields of intelligent robotics and adaptive control, making his work a valuable reference for students and researchers exploring advanced manipulator control.
Research Focus
Key Achievements
Top Papers
- 1