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Dynamic modeling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm

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Dynamic modeling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm. / Montazeri, Allahyar; West, Craig; Monk, Stephen David et al.
In: International Journal of Control, Vol. 90, No. 4, 21.09.2016, p. 661-683.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Montazeri A, West C, Monk SD, Taylor CJ. Dynamic modeling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm. International Journal of Control. 2016 Sept 21;90(4):661-683. Epub 2016 Aug 30. doi: 10.1080/00207179.2016.1230231

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Bibtex

@article{d5647787921f477c85c95c9cf4acf749,
title = "Dynamic modeling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm",
abstract = "This article concerns the problem of dynamic modeling and parameter estimation for a seven degree of freedom hydraulic manipulator. The laboratory example is a dual-manipulator mobile robotic platform used for research into nuclear decommissioning. In contrast to earlier control model orientated research using the same machine, the article develops a nonlinear, mechanistic simulation model that can subsequently be used to investigate physically meaningful disturbances. The second contribution is to optimize the parameters of the new model, i.e. to determine reliable estimates of the physical parameters of a complex robotic arm which are not known in advance. To address the nonlinear and non-convex nature of the problem, the research relies on the multi-objectivization of an output error single performance index. The developed algorithm utilises a multi-objective Genetic Algorithm (GA) in order to find a proper solution. The performance of the model and the GA is evaluated using both simulated (i.e. with a known set of {\textquoteleft}true{\textquoteright} parameters) and experimental data. Both simulation and experimental results show that multi-objectivization has improved convergence of the estimated parameters compared to the single objective output error problem formulation. This is achieved by integrating the validation phase inside the algorithm implicitly and exploiting the inherent structure of the multi-objective GA for this specific system identification problem.",
keywords = "Parameter estimation, System identification, Nonlinear model, Multi-objective genetic algorithm, Mathematical modeling",
author = "Allahyar Montazeri and Craig West and Monk, {Stephen David} and Taylor, {Charles James}",
note = "Published in Special issue on Identification and Control of Nonlinear Electro-Mechanical Systems International Journal of Control, 90:4, 641-642, http://dx.doi.org/10.1080/00207179.2017.1294824 ",
year = "2016",
month = sep,
day = "21",
doi = "10.1080/00207179.2016.1230231",
language = "English",
volume = "90",
pages = "661--683",
journal = "International Journal of Control",
issn = "0020-7179",
publisher = "Taylor and Francis Ltd.",
number = "4",

}

RIS

TY - JOUR

T1 - Dynamic modeling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm

AU - Montazeri, Allahyar

AU - West, Craig

AU - Monk, Stephen David

AU - Taylor, Charles James

N1 - Published in Special issue on Identification and Control of Nonlinear Electro-Mechanical Systems International Journal of Control, 90:4, 641-642, http://dx.doi.org/10.1080/00207179.2017.1294824

PY - 2016/9/21

Y1 - 2016/9/21

N2 - This article concerns the problem of dynamic modeling and parameter estimation for a seven degree of freedom hydraulic manipulator. The laboratory example is a dual-manipulator mobile robotic platform used for research into nuclear decommissioning. In contrast to earlier control model orientated research using the same machine, the article develops a nonlinear, mechanistic simulation model that can subsequently be used to investigate physically meaningful disturbances. The second contribution is to optimize the parameters of the new model, i.e. to determine reliable estimates of the physical parameters of a complex robotic arm which are not known in advance. To address the nonlinear and non-convex nature of the problem, the research relies on the multi-objectivization of an output error single performance index. The developed algorithm utilises a multi-objective Genetic Algorithm (GA) in order to find a proper solution. The performance of the model and the GA is evaluated using both simulated (i.e. with a known set of ‘true’ parameters) and experimental data. Both simulation and experimental results show that multi-objectivization has improved convergence of the estimated parameters compared to the single objective output error problem formulation. This is achieved by integrating the validation phase inside the algorithm implicitly and exploiting the inherent structure of the multi-objective GA for this specific system identification problem.

AB - This article concerns the problem of dynamic modeling and parameter estimation for a seven degree of freedom hydraulic manipulator. The laboratory example is a dual-manipulator mobile robotic platform used for research into nuclear decommissioning. In contrast to earlier control model orientated research using the same machine, the article develops a nonlinear, mechanistic simulation model that can subsequently be used to investigate physically meaningful disturbances. The second contribution is to optimize the parameters of the new model, i.e. to determine reliable estimates of the physical parameters of a complex robotic arm which are not known in advance. To address the nonlinear and non-convex nature of the problem, the research relies on the multi-objectivization of an output error single performance index. The developed algorithm utilises a multi-objective Genetic Algorithm (GA) in order to find a proper solution. The performance of the model and the GA is evaluated using both simulated (i.e. with a known set of ‘true’ parameters) and experimental data. Both simulation and experimental results show that multi-objectivization has improved convergence of the estimated parameters compared to the single objective output error problem formulation. This is achieved by integrating the validation phase inside the algorithm implicitly and exploiting the inherent structure of the multi-objective GA for this specific system identification problem.

KW - Parameter estimation

KW - System identification

KW - Nonlinear model

KW - Multi-objective genetic algorithm

KW - Mathematical modeling

U2 - 10.1080/00207179.2016.1230231

DO - 10.1080/00207179.2016.1230231

M3 - Journal article

VL - 90

SP - 661

EP - 683

JO - International Journal of Control

JF - International Journal of Control

SN - 0020-7179

IS - 4

ER -