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- W2783702204 abstract "Underactuated balance robots represent a broad class of mechanical systems, ranging from Furuta pendulum, autonomous motorcycles, and robotic bipedal walkers, etc. The control tasks of these systems include trajectory tracking and balancing requirements. We present a data-driven modeling and control framework of the underactuated balance robots. A machine-learning method is used to capture the dynamics and the balance equilibrium manifold that represents balancing task target. We combine the learning-based models with the structural properties of the external/internal convertible form of these underactuated systems. Applications of the proposed learning-based models and control design are applied to the Furuta pendulum by simulation and experiments." @default.
- W2783702204 created "2018-01-26" @default.
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- W2783702204 date "2017-08-01" @default.
- W2783702204 modified "2023-09-27" @default.
- W2783702204 title "Learning-based modeling and control of underactuated balance robotic systems" @default.
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- W2783702204 doi "https://doi.org/10.1109/coase.2017.8256254" @default.
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