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About

Eiho Uezato is a researcher whose work sits at the intersection of intelligent control, robotics, and evolutionary computation. His primary research areas include fuzzy control systems, underactuated manipulators, and nonholonomic systems—a class of systems that cannot be stabilized using continuous static state feedback. Uezato’s most cited paper, “Fuzzy controller for AUV robots based on machine learning and genetic algorithm” (2023, 6 citations), demonstrates his recent focus on autonomous underwater vehicles, blending machine learning with genetic algorithms to enhance robotic autonomy. His earlier contributions, such as the “Swing-up control of a 3-DOF acrobot using an evolutionary approach” and “Intelligent control of a three-DOF planar underactuated manipulator” (both 2009, 5 citations each), showcase his expertise in controlling complex, underactuated robotic systems. Notably, his work on “A discontinuous control of a nonholonomic wheeled mobile robot” (2009, 3 citations) addresses a fundamental challenge in robotics: achieving asymptotic stability in nonholonomic systems, where traditional continuous feedback fails. Uezato’s research, though modest in citation counts, provides foundational insights into evolutionary and neurocontrol strategies for challenging robotic platforms, making him a valuable contributor to the field of intelligent robotics and control theory.