Kazuya Sato
Papers
10
Total Citations
58
H-Index
5
About
Kazuya Sato is a control systems engineer whose research spans robust adaptive control theory, robotic manipulator dynamics, and autonomous mobile robotics. His most significant contributions lie in developing advanced control strategies for robotic systems operating under uncertainty — particularly addressing the challenging problem of input torque uncertainty in robotic manipulators. Through a series of influential works beginning in the mid-2000s, Sato systematically advanced adaptive H∞ control methods capable of compensating for dead-zone effects, link frictions, and external disturbances, with his 2008 paper on adaptive H∞ control for robotic manipulators becoming his most-cited work with nine citations. His research then expanded to nonholonomic wheeled mobile robots, where he developed robust adaptive trajectory tracking strategies that account for input uncertainty in dynamic control systems. More recently, Sato has embraced modern machine learning approaches, publishing work on deep learning-based marker recognition for autonomous robot navigation in GPS-denied environments. Collectively accumulating over 58 citations across his body of work, his research bridges classical robust control theory and contemporary intelligent robotics, offering practical solutions for high-speed, high-precision industrial and autonomous robotic applications.
Research Focus
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