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    Rights statement: This is the author’s version of a work that was accepted for publication in Control Engineering Practice. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Control Engineering Practice, 74, 2018 DOI: 10.1016/j.conengprac.2018.02.010

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An improved instrumental variable method for industrial robot model identification

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An improved instrumental variable method for industrial robot model identification. / Brunot, M.; Janot, A.; Young, P.C.; Carrillo, F.

In: Control Engineering Practice, Vol. 74, 05.2018, p. 107-117.

Research output: Contribution to journalJournal articlepeer-review

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Brunot, M, Janot, A, Young, PC & Carrillo, F 2018, 'An improved instrumental variable method for industrial robot model identification', Control Engineering Practice, vol. 74, pp. 107-117. https://doi.org/10.1016/j.conengprac.2018.02.010

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Brunot, M. ; Janot, A. ; Young, P.C. ; Carrillo, F. / An improved instrumental variable method for industrial robot model identification. In: Control Engineering Practice. 2018 ; Vol. 74. pp. 107-117.

Bibtex

@article{064675f1b76d4be09c4f205b06f5c48c,
title = "An improved instrumental variable method for industrial robot model identification",
abstract = "Abstract Industrial robots are electro-mechanical systems with double integrator behaviour, necessitating operation and model identification under closed-loop control conditions. The Inverse Dynamic Identification Model (IDIM) is a mechanical model based on Newton{\textquoteright}s laws that has the advantage of being linear with respect to the parameters. Existing Instrumental Variable (IDIM-IV) estimation provides a robust solution to this estimation problem and the paper introduces an improved IDIM-PIV method that takes account of the additive noise characteristics by adding prefilters that provide lower variance estimates of the IDIM parameters. Inspired by the prefiltering approach used in optimal Refined Instrumental Variable (RIV) estimation, the IDIM-PIV method identifies the nonlinear physical model of the robot, as well as the noise model resulting from the feedback control system. It also has the advantage of providing a systematic prefiltering process, in contrast to that required for the previous IDIM-IV method. The issue of an unknown controller is also considered and resolved using existing parametric identification. The evaluation of the new estimation algorithms on a six degrees-of-freedom rigid robot shows that they improve statistical efficiency, with the controller either known or identified as an intrinsic part of the IDIM-PIV algorithm.",
keywords = "Refined instrumental variable, Closed-loop system identification, Robot identification, Inverse dynamics, Dynamic parameters, Robot dynamics",
author = "M. Brunot and A. Janot and P.C. Young and F. Carrillo",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Control Engineering Practice. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Control Engineering Practice, 74, 2018 DOI: 10.1016/j.conengprac.2018.02.010",
year = "2018",
month = may,
doi = "10.1016/j.conengprac.2018.02.010",
language = "English",
volume = "74",
pages = "107--117",
journal = "Control Engineering Practice",
issn = "0967-0661",
publisher = "Elsevier Limited",

}

RIS

TY - JOUR

T1 - An improved instrumental variable method for industrial robot model identification

AU - Brunot, M.

AU - Janot, A.

AU - Young, P.C.

AU - Carrillo, F.

N1 - This is the author’s version of a work that was accepted for publication in Control Engineering Practice. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Control Engineering Practice, 74, 2018 DOI: 10.1016/j.conengprac.2018.02.010

PY - 2018/5

Y1 - 2018/5

N2 - Abstract Industrial robots are electro-mechanical systems with double integrator behaviour, necessitating operation and model identification under closed-loop control conditions. The Inverse Dynamic Identification Model (IDIM) is a mechanical model based on Newton’s laws that has the advantage of being linear with respect to the parameters. Existing Instrumental Variable (IDIM-IV) estimation provides a robust solution to this estimation problem and the paper introduces an improved IDIM-PIV method that takes account of the additive noise characteristics by adding prefilters that provide lower variance estimates of the IDIM parameters. Inspired by the prefiltering approach used in optimal Refined Instrumental Variable (RIV) estimation, the IDIM-PIV method identifies the nonlinear physical model of the robot, as well as the noise model resulting from the feedback control system. It also has the advantage of providing a systematic prefiltering process, in contrast to that required for the previous IDIM-IV method. The issue of an unknown controller is also considered and resolved using existing parametric identification. The evaluation of the new estimation algorithms on a six degrees-of-freedom rigid robot shows that they improve statistical efficiency, with the controller either known or identified as an intrinsic part of the IDIM-PIV algorithm.

AB - Abstract Industrial robots are electro-mechanical systems with double integrator behaviour, necessitating operation and model identification under closed-loop control conditions. The Inverse Dynamic Identification Model (IDIM) is a mechanical model based on Newton’s laws that has the advantage of being linear with respect to the parameters. Existing Instrumental Variable (IDIM-IV) estimation provides a robust solution to this estimation problem and the paper introduces an improved IDIM-PIV method that takes account of the additive noise characteristics by adding prefilters that provide lower variance estimates of the IDIM parameters. Inspired by the prefiltering approach used in optimal Refined Instrumental Variable (RIV) estimation, the IDIM-PIV method identifies the nonlinear physical model of the robot, as well as the noise model resulting from the feedback control system. It also has the advantage of providing a systematic prefiltering process, in contrast to that required for the previous IDIM-IV method. The issue of an unknown controller is also considered and resolved using existing parametric identification. The evaluation of the new estimation algorithms on a six degrees-of-freedom rigid robot shows that they improve statistical efficiency, with the controller either known or identified as an intrinsic part of the IDIM-PIV algorithm.

KW - Refined instrumental variable

KW - Closed-loop system identification

KW - Robot identification

KW - Inverse dynamics

KW - Dynamic parameters

KW - Robot dynamics

U2 - 10.1016/j.conengprac.2018.02.010

DO - 10.1016/j.conengprac.2018.02.010

M3 - Journal article

VL - 74

SP - 107

EP - 117

JO - Control Engineering Practice

JF - Control Engineering Practice

SN - 0967-0661

ER -