Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Control on 30/08/2016, available online: http://www.tandfonline.com/10.1080/00207179.2016.1230231
Accepted author manuscript, 1.64 MB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -