Masatoshi Tokita
National Institute of Technology, Kisarazu College, Nagoya University
Papers
19
Total Citations
255
H-Index
10
About
Masatoshi Tokita is a pioneering researcher in intelligent robotics and neural network-based control systems, whose work has fundamentally shaped how adaptive controllers are designed for robotic manipulators. Beginning in the late 1980s, Tokita was among the first to apply neural network models to force control problems in robotics — a contribution documented in his influential 1989 paper on variable-gain control using perceptron-based networks, which has garnered 38 citations. Throughout the early 1990s, he developed a robust body of work on neuromorphic control, demonstrating how multi-layer neural networks could enable robotic systems to adapt and learn in uncertain, nonlinear environments across both position and force servo applications. His 2003 work on fuzzy neural network architectures — his most cited contribution with 40 citations — represents a significant evolution in his thinking, integrating symbolic, knowledge-based reasoning with neuromorphic control into cohesive hierarchical intelligent systems. Across more than a decade of research, Tokita consistently pursued the challenge of making robotic manipulators smarter, more adaptable, and more capable of handling real-world dynamics, accumulating over 229 citations and establishing himself as a foundational voice in intelligent robotic control.
Research Focus
Key Achievements
Top Papers
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- 3Neuromorphic control: adaptation and learning36 citations · 1992
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- 6Adaptation and learning for robotic manipulator by neural network18 citations · 1990
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