Online learning of virtual impedance parameters in non-contact impedance control using neural networks

被引:20
作者
Tsuji, T [1 ]
Terauchi, M
Tanaka, Y
机构
[1] Hiroshima Univ, Dept Artif Complex Syst Engn, Hiroshima 7398527, Japan
[2] Hiroshima Int Univ, Dept Kansei Informat, Hiroshima 7240695, Japan
来源
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS | 2004年 / 34卷 / 05期
关键词
impact control; impedance control; noncontact impedance; neural networks (NN); robot manipulator;
D O I
10.1109/TSMCB.2004.829133
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Impedance control is one of the most effective methods for controlling the interaction between a manipulator and a task environment. In conventional impedance control methods, however, the manipulator cannot be controlled until the end-effector contacts task environments. A noncontact impedance control method has been proposed to resolve such a problem. This method on only can regulate the end-point impedance, but also the virtual impedance that works between the manipulator and the environment by using visual information. This paper proposes a learning method using neural networks to regulate the virtual impedance parameters according to a given task. The validity of the proposed method was verified through computer simulations and experiments with a multijoint robotic manipulator.
引用
收藏
页码:2112 / 2118
页数:7
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