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

Published online by Cambridge University Press:  12 October 2010

João P. Ferreira ,
Manuel Crisóstomo  and
A. Paulo Coimbra
João P. Ferreira*
Affiliation:
Department of Electrical Engineering, Superior Institute of Engineering of Coimbra, Rua Pedro Nunes-Quinta da Nora, Coimbra 3030-199, Portugal Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Polo 2-Pinhal de Marrocos, Coimbra 3030-290, Portugal. E-mail: mcris@isr.uc.pt, acoimbra@deec.uc.pt
Manuel Crisóstomo
Affiliation:
Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Polo 2-Pinhal de Marrocos, Coimbra 3030-290, Portugal. E-mail: mcris@isr.uc.pt, acoimbra@deec.uc.pt
A. Paulo Coimbra
Affiliation:
Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Polo 2-Pinhal de Marrocos, Coimbra 3030-290, Portugal. E-mail: mcris@isr.uc.pt, acoimbra@deec.uc.pt
*
*Corresponding author. E-mail: ferreira@isec.pt
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Summary

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.

The ZMP is calculated by reading four force sensors placed under each robot's foot. The gait implemented in this biped is based on ankle and hip human trajectories that were acquired and adapted to the robot's size. Some experiments are presented and the results show that the implemented gait combined either with the SVR controller or with the TSK NF network controller can be used to control this biped robot. The SVR and the NF controllers exhibit similar stability, but the SVR controller runs at 0.2 ms, about 50 times faster than the NF controller and much faster than a controller based on full ZMP dynamic model equations.

Keywords

Neural-fuzzy (NF) networksSupport vector regression (SVR)Biped robotBalanceZero-moment point (ZMP)
Type
Articles
Information
Robotica , Volume 29 , Issue 5 , September 2011 , pp. 717 - 731
Copyright
Copyright © Cambridge University Press 2010

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