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A genetic algorithm approach for parameter optimization of a 7DOF robotic manipulator

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

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A genetic algorithm approach for parameter optimization of a 7DOF robotic manipulator. / West, Craig; Montazeri, Allahyar; Monk, Stephen David; Taylor, Charles James.

2016. Paper presented at 8th IFAC Conference on Manufacturing Modelling, Management and Control, Troyes, France.

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

Harvard

West, C, Montazeri, A, Monk, SD & Taylor, CJ 2016, 'A genetic algorithm approach for parameter optimization of a 7DOF robotic manipulator', Paper presented at 8th IFAC Conference on Manufacturing Modelling, Management and Control, Troyes, France, 28/06/16. https://doi.org/10.1016/j.ifacol.2016.07.688

APA

Vancouver

West C, Montazeri A, Monk SD, Taylor CJ. A genetic algorithm approach for parameter optimization of a 7DOF robotic manipulator. 2016. Paper presented at 8th IFAC Conference on Manufacturing Modelling, Management and Control, Troyes, France. https://doi.org/10.1016/j.ifacol.2016.07.688

Author

West, Craig ; Montazeri, Allahyar ; Monk, Stephen David ; Taylor, Charles James. / A genetic algorithm approach for parameter optimization of a 7DOF robotic manipulator. Paper presented at 8th IFAC Conference on Manufacturing Modelling, Management and Control, Troyes, France.

Bibtex

@conference{2101e922044d445eae6135a63ba087bd,
title = "A genetic algorithm approach for parameter optimization of a 7DOF robotic manipulator",
abstract = "In this paper the problem of dynamic modeling and parameter estimation of a seven degree of freedom hydraulic manipulator is investigated. The numerical model is developed in Simulink using SimMechanic and Simscape toolboxes with unknown/uncertain parameters. The aim of this paper is to develop a mechanism that enables us to find a feasible set of parameters for the robot that is consistent with measurements of the input, output, and states of the system under noisy and unknown operating conditions. As the first step a genetic algorithm is developed to solve an output error system identification problem for a specific joint, i.e. joint 2, such that the parameters of the joint converge to the desired set of parameters within an acceptable accuracy. The results can be straightforwardly extended to all joints of the manipulator",
keywords = "Parameter estimation, System identification, Nonlinear model, Genetic algorithm, Mathematical modeling",
author = "Craig West and Allahyar Montazeri and Monk, {Stephen David} and Taylor, {Charles James}",
year = "2016",
month = jun,
day = "28",
doi = "10.1016/j.ifacol.2016.07.688",
language = "English",
note = "8th IFAC Conference on Manufacturing Modelling, Management and Control ; Conference date: 28-06-2016",

}

RIS

TY - CONF

T1 - A genetic algorithm approach for parameter optimization of a 7DOF robotic manipulator

AU - West, Craig

AU - Montazeri, Allahyar

AU - Monk, Stephen David

AU - Taylor, Charles James

PY - 2016/6/28

Y1 - 2016/6/28

N2 - In this paper the problem of dynamic modeling and parameter estimation of a seven degree of freedom hydraulic manipulator is investigated. The numerical model is developed in Simulink using SimMechanic and Simscape toolboxes with unknown/uncertain parameters. The aim of this paper is to develop a mechanism that enables us to find a feasible set of parameters for the robot that is consistent with measurements of the input, output, and states of the system under noisy and unknown operating conditions. As the first step a genetic algorithm is developed to solve an output error system identification problem for a specific joint, i.e. joint 2, such that the parameters of the joint converge to the desired set of parameters within an acceptable accuracy. The results can be straightforwardly extended to all joints of the manipulator

AB - In this paper the problem of dynamic modeling and parameter estimation of a seven degree of freedom hydraulic manipulator is investigated. The numerical model is developed in Simulink using SimMechanic and Simscape toolboxes with unknown/uncertain parameters. The aim of this paper is to develop a mechanism that enables us to find a feasible set of parameters for the robot that is consistent with measurements of the input, output, and states of the system under noisy and unknown operating conditions. As the first step a genetic algorithm is developed to solve an output error system identification problem for a specific joint, i.e. joint 2, such that the parameters of the joint converge to the desired set of parameters within an acceptable accuracy. The results can be straightforwardly extended to all joints of the manipulator

KW - Parameter estimation

KW - System identification

KW - Nonlinear model

KW - Genetic algorithm

KW - Mathematical modeling

U2 - 10.1016/j.ifacol.2016.07.688

DO - 10.1016/j.ifacol.2016.07.688

M3 - Conference paper

T2 - 8th IFAC Conference on Manufacturing Modelling, Management and Control

Y2 - 28 June 2016

ER -