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A UKF-Based Predictable SVR Learning Controller for Biped Walking
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DOI：10.1109/TSMC.2013.2242887.png)
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Abstract
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An unscented Kalman filter (UKF)-based predictable support vector regression (SVR) learning controller is proposed to improve the flexibility of biped walking robots. After estimating the biped states of the next moment using a UKF, an SVR learning controller with the predicted biped states is implemented to ensure the zero moment point (ZMP) stability. Using the predicted biped states, the SVR learning controller can predictably adjust the posture of the trunk timely and properly to adapt to the dynamic posture of the whole body. The flexibility of biped robots is enhanced by the proposed method, which is promising for realizing the stable biped walking in unstructured environments. Simulation and experimental results demonstrate the superiority of the proposed methods.
Keywords:
Biped robot
gait control
learning control
state prediction
support vector regression (SVR)
unscented Kalman filter (UKF)
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