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Software sensor to enhance online parametric identification for nonlinear closed-loop systems for robotic applications

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Software sensor to enhance online parametric identification for nonlinear closed-loop systems for robotic applications. / Sidhom, Lilia; Chihi, Ines; Kamavuako, Ernest Nlandu.

In: SENSORS, Vol. 21, No. 11, 3653, 01.06.2021.

Research output: Contribution to journalArticlepeer-review

Harvard

Sidhom, L, Chihi, I & Kamavuako, EN 2021, 'Software sensor to enhance online parametric identification for nonlinear closed-loop systems for robotic applications', SENSORS, vol. 21, no. 11, 3653. https://doi.org/10.3390/s21113653

APA

Sidhom, L., Chihi, I., & Kamavuako, E. N. (2021). Software sensor to enhance online parametric identification for nonlinear closed-loop systems for robotic applications. SENSORS, 21(11), [3653]. https://doi.org/10.3390/s21113653

Vancouver

Sidhom L, Chihi I, Kamavuako EN. Software sensor to enhance online parametric identification for nonlinear closed-loop systems for robotic applications. SENSORS. 2021 Jun 1;21(11). 3653. https://doi.org/10.3390/s21113653

Author

Sidhom, Lilia ; Chihi, Ines ; Kamavuako, Ernest Nlandu. / Software sensor to enhance online parametric identification for nonlinear closed-loop systems for robotic applications. In: SENSORS. 2021 ; Vol. 21, No. 11.

Bibtex Download

@article{8a66d99cc9b2404cacbc42784bce0cf1,
title = "Software sensor to enhance online parametric identification for nonlinear closed-loop systems for robotic applications",
abstract = "This paper proposes an online direct closed-loop identification method based on a new dynamic sliding mode technique for robotic applications. The estimated parameters are obtained by minimizing the prediction error with respect to the vector of unknown parameters. The estimation step requires knowledge of the actual input and output of the system, as well as the successive estimate of the output derivatives. Therefore, a special robust differentiator based on higher-order sliding modes with a dynamic gain is defined. A proof of convergence is given for the robust differ-entiator. The dynamic parameters are estimated using the recursive least squares algorithm by the solution of a system model that is obtained from sampled positions along the closed-loop trajectory. An experimental validation is given for a 2 Degrees Of Freedom (2-DOF) robot manipulator, where direct and cross-validations are carried out. A comparative analysis is detailed to evaluate the algo-rithm{\textquoteright}s effectiveness and reliability. Its performance is demonstrated by a better-quality torque prediction compared to other differentiators recently proposed in the literature. The experimental results highlight that the differentiator design strongly influences the online parametric identification and, thus, the prediction of system input variables.",
keywords = "Direct and cross-validation, Dynamic sliding mode, Identification, Robot application",
author = "Lilia Sidhom and Ines Chihi and Kamavuako, {Ernest Nlandu}",
note = "Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2021",
month = jun,
day = "1",
doi = "10.3390/s21113653",
language = "English",
volume = "21",
journal = "SENSORS",
issn = "1424-8220",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "11",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Software sensor to enhance online parametric identification for nonlinear closed-loop systems for robotic applications

AU - Sidhom, Lilia

AU - Chihi, Ines

AU - Kamavuako, Ernest Nlandu

N1 - Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/6/1

Y1 - 2021/6/1

N2 - This paper proposes an online direct closed-loop identification method based on a new dynamic sliding mode technique for robotic applications. The estimated parameters are obtained by minimizing the prediction error with respect to the vector of unknown parameters. The estimation step requires knowledge of the actual input and output of the system, as well as the successive estimate of the output derivatives. Therefore, a special robust differentiator based on higher-order sliding modes with a dynamic gain is defined. A proof of convergence is given for the robust differ-entiator. The dynamic parameters are estimated using the recursive least squares algorithm by the solution of a system model that is obtained from sampled positions along the closed-loop trajectory. An experimental validation is given for a 2 Degrees Of Freedom (2-DOF) robot manipulator, where direct and cross-validations are carried out. A comparative analysis is detailed to evaluate the algo-rithm’s effectiveness and reliability. Its performance is demonstrated by a better-quality torque prediction compared to other differentiators recently proposed in the literature. The experimental results highlight that the differentiator design strongly influences the online parametric identification and, thus, the prediction of system input variables.

AB - This paper proposes an online direct closed-loop identification method based on a new dynamic sliding mode technique for robotic applications. The estimated parameters are obtained by minimizing the prediction error with respect to the vector of unknown parameters. The estimation step requires knowledge of the actual input and output of the system, as well as the successive estimate of the output derivatives. Therefore, a special robust differentiator based on higher-order sliding modes with a dynamic gain is defined. A proof of convergence is given for the robust differ-entiator. The dynamic parameters are estimated using the recursive least squares algorithm by the solution of a system model that is obtained from sampled positions along the closed-loop trajectory. An experimental validation is given for a 2 Degrees Of Freedom (2-DOF) robot manipulator, where direct and cross-validations are carried out. A comparative analysis is detailed to evaluate the algo-rithm’s effectiveness and reliability. Its performance is demonstrated by a better-quality torque prediction compared to other differentiators recently proposed in the literature. The experimental results highlight that the differentiator design strongly influences the online parametric identification and, thus, the prediction of system input variables.

KW - Direct and cross-validation

KW - Dynamic sliding mode

KW - Identification

KW - Robot application

UR - http://www.scopus.com/inward/record.url?scp=85106298393&partnerID=8YFLogxK

U2 - 10.3390/s21113653

DO - 10.3390/s21113653

M3 - Article

AN - SCOPUS:85106298393

VL - 21

JO - SENSORS

JF - SENSORS

SN - 1424-8220

IS - 11

M1 - 3653

ER -

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