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JRM Vol.19 No.1 pp. 106-113
doi: 10.20965/jrm.2007.p0106
(2007)

Paper:

Simultaneous Learning of Robot Impedance Parameters Using Neural Networks

Mutsuhiro Terauchi*, Yoshiyuki Tanaka**,
Seishiro Sakaguchi**, Nan Bu***,
and Toshio Tsuji**

*Faculty of Psychological Sciences, Hiroshima International University, 555-36 Kurose-Gakuendai, Higashi-Hiroshima, Hiroshima 724-0695, Japan

**Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan

***National Institute of Advanced Industrial Science and Technology, 807-1 Shunku, Tosu, Saga 841-0052, Japan

Received:
June 6, 2006
Accepted:
July 31, 2006
Published:
February 20, 2007
Keywords:
robot manipulator, impedance control, neural networks, learning of impedance parameters
Abstract

Impedance control is one of the most effective control methods for interaction between a robotic manipulator and its environment. Robot impedance control regulates the response of the manipulator to contact and virtual impedance control regulates the manipulator’s response before contact. Although these impedance parameters may be regulated using neural networks, conventional methods do not consider regulating robot impedance and virtual impedance simultaneously. This paper proposes a simultaneous learning method to regulate the impedance parameters using neural networks. The validity of the proposed method is demonstrated in computer simulations of tasks by a multi-joint robotic manipulator.

Cite this article as:
Mutsuhiro Terauchi, Yoshiyuki Tanaka,
Seishiro Sakaguchi, Nan Bu, and
and Toshio Tsuji, “Simultaneous Learning of Robot Impedance Parameters Using Neural Networks,” J. Robot. Mechatron., Vol.19, No.1, pp. 106-113, 2007.
Data files:
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