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Gait generation via unified learning optimal control of Hamiltonian systems

Published online by Cambridge University Press:  23 January 2013

Satoshi Satoh ,
Kenji Fujimoto  and
Sang-Ho Hyon
Satoshi Satoh*
Affiliation:
Division of Mechanical Systems and Applied Mechanics, Faculty of Engineering, Hiroshima University, 1-4-1, Kagamiyama, Higashi-Hiroshima 739-8527, Japan
Kenji Fujimoto
Affiliation:
Department of Aerospace Engineering, Graduate School of Engineering, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan
Sang-Ho Hyon
Affiliation:
Department of Robotics, Ritsumeikan University, Noji Higashi 1-1-1, Kusatsu, Shiga 525-8577, Japan
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper proposes a repetitive control type optimal gait generation framework by executing learning control and parameter tuning. We propose a learning optimal control method of Hamiltonian systems unifying iterative learning control (ILC) and iterative feedback tuning (IFT). It allows one to simultaneously obtain an optimal feedforward input and tuning parameter for a plant system, which minimizes a given cost function. In the proposed method, a virtual constraint by a potential energy prevents a biped robot from falling. The strength of the constraint is automatically mitigated by the IFT part of the proposed method, according to the progress of trajectory learning by the ILC part.

Keywords

Gait generationBiped robotsRepetitive controlIterative learning controlHamiltonian systems
Type
Articles
Information
Robotica , Volume 31 , Issue 5 , August 2013 , pp. 717 - 732
Copyright
Copyright © Cambridge University Press 2013 

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