Experimental comparison of parameter estimation methods in adaptive robot control

Harry Berghuis, Harry Berghuis, Herman Roebbers, Herman Roebbers, Henk Nijmeijer

Research output: Contribution to journalArticleAcademicpeer-review

24 Citations (Scopus)
89 Downloads (Pure)

Abstract

In the literature on adaptive robot control a large variety of parameter estimation methods have been proposed, ranging from tracking-error-driven gradient methods to combined tracking- and prediction-error-driven least-squares type adaptation methods. This paper presents experimental data from a comparative study between these adaptation methods, performed on a two-degrees-of-freedom robot manipulator. Our results show that the prediction error concept is sensitive to unavoidable model uncertainties. We also demonstrate empirically the fast convergence properties of least-squares adaptation relative to gradient approaches. However, in view of the noise sensitivity of the least-squares method, the marginal performance benefits, and the computational burden, we (cautiously) conclude that the tracking-error driven gradient method is preferred for parameter adaptation in robotic applications.
Original languageEnglish
Pages (from-to)1275-1285
Number of pages10
JournalAutomatica
Volume31
Issue number9
DOIs
Publication statusPublished - 1995

Fingerprint

Intelligent robots
Parameter estimation
Gradient methods
Manipulators
Robotics
Robots

Keywords

  • METIS-111802
  • IR-14816

Cite this

Berghuis, H., Berghuis, H., Roebbers, H., Roebbers, H., & Nijmeijer, H. (1995). Experimental comparison of parameter estimation methods in adaptive robot control. Automatica, 31(9), 1275-1285. https://doi.org/10.1016/0005-1098(95)00046-Y
Berghuis, Harry ; Berghuis, Harry ; Roebbers, Herman ; Roebbers, Herman ; Nijmeijer, Henk. / Experimental comparison of parameter estimation methods in adaptive robot control. In: Automatica. 1995 ; Vol. 31, No. 9. pp. 1275-1285.
@article{10a34977c2f64839946ed831731dd6dd,
title = "Experimental comparison of parameter estimation methods in adaptive robot control",
abstract = "In the literature on adaptive robot control a large variety of parameter estimation methods have been proposed, ranging from tracking-error-driven gradient methods to combined tracking- and prediction-error-driven least-squares type adaptation methods. This paper presents experimental data from a comparative study between these adaptation methods, performed on a two-degrees-of-freedom robot manipulator. Our results show that the prediction error concept is sensitive to unavoidable model uncertainties. We also demonstrate empirically the fast convergence properties of least-squares adaptation relative to gradient approaches. However, in view of the noise sensitivity of the least-squares method, the marginal performance benefits, and the computational burden, we (cautiously) conclude that the tracking-error driven gradient method is preferred for parameter adaptation in robotic applications.",
keywords = "METIS-111802, IR-14816",
author = "Harry Berghuis and Harry Berghuis and Herman Roebbers and Herman Roebbers and Henk Nijmeijer",
year = "1995",
doi = "10.1016/0005-1098(95)00046-Y",
language = "English",
volume = "31",
pages = "1275--1285",
journal = "Automatica",
issn = "0005-1098",
publisher = "Elsevier",
number = "9",

}

Berghuis, H, Berghuis, H, Roebbers, H, Roebbers, H & Nijmeijer, H 1995, 'Experimental comparison of parameter estimation methods in adaptive robot control' Automatica, vol. 31, no. 9, pp. 1275-1285. https://doi.org/10.1016/0005-1098(95)00046-Y

Experimental comparison of parameter estimation methods in adaptive robot control. / Berghuis, Harry; Berghuis, Harry; Roebbers, Herman; Roebbers, Herman; Nijmeijer, Henk.

In: Automatica, Vol. 31, No. 9, 1995, p. 1275-1285.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Experimental comparison of parameter estimation methods in adaptive robot control

AU - Berghuis, Harry

AU - Berghuis, Harry

AU - Roebbers, Herman

AU - Roebbers, Herman

AU - Nijmeijer, Henk

PY - 1995

Y1 - 1995

N2 - In the literature on adaptive robot control a large variety of parameter estimation methods have been proposed, ranging from tracking-error-driven gradient methods to combined tracking- and prediction-error-driven least-squares type adaptation methods. This paper presents experimental data from a comparative study between these adaptation methods, performed on a two-degrees-of-freedom robot manipulator. Our results show that the prediction error concept is sensitive to unavoidable model uncertainties. We also demonstrate empirically the fast convergence properties of least-squares adaptation relative to gradient approaches. However, in view of the noise sensitivity of the least-squares method, the marginal performance benefits, and the computational burden, we (cautiously) conclude that the tracking-error driven gradient method is preferred for parameter adaptation in robotic applications.

AB - In the literature on adaptive robot control a large variety of parameter estimation methods have been proposed, ranging from tracking-error-driven gradient methods to combined tracking- and prediction-error-driven least-squares type adaptation methods. This paper presents experimental data from a comparative study between these adaptation methods, performed on a two-degrees-of-freedom robot manipulator. Our results show that the prediction error concept is sensitive to unavoidable model uncertainties. We also demonstrate empirically the fast convergence properties of least-squares adaptation relative to gradient approaches. However, in view of the noise sensitivity of the least-squares method, the marginal performance benefits, and the computational burden, we (cautiously) conclude that the tracking-error driven gradient method is preferred for parameter adaptation in robotic applications.

KW - METIS-111802

KW - IR-14816

U2 - 10.1016/0005-1098(95)00046-Y

DO - 10.1016/0005-1098(95)00046-Y

M3 - Article

VL - 31

SP - 1275

EP - 1285

JO - Automatica

JF - Automatica

SN - 0005-1098

IS - 9

ER -