Online Learning of Virtual Impedance Parameters in Non-Contact Impedance Control Using Neural Networks

IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics Volume 34 Issue 5 Page 2112-2118 published_at 2004
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Title ( eng )
Online Learning of Virtual Impedance Parameters in Non-Contact Impedance Control Using Neural Networks
Creator
Terauchi Mutsuhiro
Source Title
IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics
Volume 34
Issue 5
Start Page 2112
End Page 2118
Abstract
Impedance control is one of the most effective methods forcontrolling the interaction between a manipulator and a task environment.In conventional impedance control methods, however, the manipulatorcannot be controlled until the end-effector contacts task environments. Anoncontact impedance control method has been proposed to resolve such aproblem. This method on only can regulate the end-point impedance, butalso the virtual impedance that works between the manipulator and theenvironment by using visual information. This paper proposes a learningmethod using neural networks to regulate the virtual impedance parametersaccording to a given task. The validity of the proposed method wasverified through computer simulations and experiments with a multijointrobotic manipulator.
Keywords
Impact control
impedance control
noncontact impedance
neural networks (NN)
robot manipulator
NDC
Mechanical engineering [ 530 ]
Language
eng
Resource Type journal article
Publisher
IEEE
Date of Issued 2004
Rights
Copyright (c) 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Publish Type Version of Record
Access Rights open access
Source Identifier
[ISSN] 1083-4419
[DOI] http://dx.doi.org/10.1109/TSMCB.2004.829133 isVersionOf