Sagittal stability PD controllers for a biped robot using a neurofuzzy network and an SVR

Publisher: Cambridge University Press

E-ISSN: 1469-8668|29|5|717-731

ISSN: 0263-5747

Source: Robotica, Vol.29, Iss.5, 2011-09, pp. : 717-731

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Abstract

The real-time balance PD control of an eight-link biped robot using a zero-moment point (ZMP) dynamic model is implemented using two alternative intelligent computing control techniques that were compared: one based on support vector regression (SVR) and another based on a first order Takagi–Sugeno–Kang (TSK) -type neural-fuzzy (NF). Both methods use the ZMP error, and its variation as inputs and the output is the correction of the robot's torso necessary for its sagittal balance. The SVR and the NF were trained based on simulation data, and their performance was verified with a real biped robot. Two performance indexes are proposed to evaluate and compare the online performance of the two control methods.