Modeling and parameter identification of seated human body with the reference vector guided evolutionary algorithm

The low frequency vibration of the vehicle in motion has a great influence on the ride comfort of occupants. The research on the vibration response characteristics of human body plays a great role in analyzing and improving ride comfort. The purpose of this study was to investigate the parameter ide...

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Autores principales:	Shuguang Zhang, Wenku Shi, Zhiyong Chen
Formato:	article
Lenguaje:	EN
Publicado:	SAGE Publishing 2021
Materias:	Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/c436fba4b5a74733a0735315f29d8ba4
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spelling	oai:doaj.org-article:c436fba4b5a74733a0735315f29d8ba42021-12-02T03:33:22ZModeling and parameter identification of seated human body with the reference vector guided evolutionary algorithm1687-814010.1177/16878140211062679https://doaj.org/article/c436fba4b5a74733a0735315f29d8ba42021-11-01T00:00:00Zhttps://doi.org/10.1177/16878140211062679https://doaj.org/toc/1687-8140The low frequency vibration of the vehicle in motion has a great influence on the ride comfort of occupants. The research on the vibration response characteristics of human body plays a great role in analyzing and improving ride comfort. The purpose of this study was to investigate the parameter identification of seated human body dynamic model. A seven-degree-of-freedom (DOF) lumped parameter model was established to describe the vibration response characteristics of human body. The derivation processes of apparent mass (AM) and seat to head transmissibility (STHT) were performed. After the theoretical calculation of the human body vibration characteristics, we used several different evolutionary algorithms to identify the 23 parameters of the model, including the mass, stiffness and damping parameters. By comparing the results of the five optimization algorithms and comprehensively analyzing the convergence and distribution of the non-dominated solution set, we found that the reference vector guided evolutionary algorithm (RVEA) shows good competitiveness in solving many-objective optimization problem (MaOP), that is, parameter identification of seated human body model in this paper. The AM and STHT calculated by model identification were compared with their measured by experiment. The result shows that the selected seven-DOF model can well describe the vertical vibration characteristics of seated human body and the identification method used in this paper can accurately identify the parameters of lumped parameter model, which provides convenience for the establishment of a complete “road-vehicle-seat-human body” system dynamic model.Shuguang ZhangWenku ShiZhiyong ChenSAGE PublishingarticleMechanical engineering and machineryTJ1-1570ENAdvances in Mechanical Engineering, Vol 13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mechanical engineering and machinery TJ1-1570
spellingShingle	Mechanical engineering and machinery TJ1-1570 Shuguang Zhang Wenku Shi Zhiyong Chen Modeling and parameter identification of seated human body with the reference vector guided evolutionary algorithm
description	The low frequency vibration of the vehicle in motion has a great influence on the ride comfort of occupants. The research on the vibration response characteristics of human body plays a great role in analyzing and improving ride comfort. The purpose of this study was to investigate the parameter identification of seated human body dynamic model. A seven-degree-of-freedom (DOF) lumped parameter model was established to describe the vibration response characteristics of human body. The derivation processes of apparent mass (AM) and seat to head transmissibility (STHT) were performed. After the theoretical calculation of the human body vibration characteristics, we used several different evolutionary algorithms to identify the 23 parameters of the model, including the mass, stiffness and damping parameters. By comparing the results of the five optimization algorithms and comprehensively analyzing the convergence and distribution of the non-dominated solution set, we found that the reference vector guided evolutionary algorithm (RVEA) shows good competitiveness in solving many-objective optimization problem (MaOP), that is, parameter identification of seated human body model in this paper. The AM and STHT calculated by model identification were compared with their measured by experiment. The result shows that the selected seven-DOF model can well describe the vertical vibration characteristics of seated human body and the identification method used in this paper can accurately identify the parameters of lumped parameter model, which provides convenience for the establishment of a complete “road-vehicle-seat-human body” system dynamic model.
format	article
author	Shuguang Zhang Wenku Shi Zhiyong Chen
author_facet	Shuguang Zhang Wenku Shi Zhiyong Chen
author_sort	Shuguang Zhang
title	Modeling and parameter identification of seated human body with the reference vector guided evolutionary algorithm
title_short	Modeling and parameter identification of seated human body with the reference vector guided evolutionary algorithm
title_full	Modeling and parameter identification of seated human body with the reference vector guided evolutionary algorithm
title_fullStr	Modeling and parameter identification of seated human body with the reference vector guided evolutionary algorithm
title_full_unstemmed	Modeling and parameter identification of seated human body with the reference vector guided evolutionary algorithm
title_sort	modeling and parameter identification of seated human body with the reference vector guided evolutionary algorithm
publisher	SAGE Publishing
publishDate	2021
url	https://doaj.org/article/c436fba4b5a74733a0735315f29d8ba4
work_keys_str_mv	AT shuguangzhang modelingandparameteridentificationofseatedhumanbodywiththereferencevectorguidedevolutionaryalgorithm AT wenkushi modelingandparameteridentificationofseatedhumanbodywiththereferencevectorguidedevolutionaryalgorithm AT zhiyongchen modelingandparameteridentificationofseatedhumanbodywiththereferencevectorguidedevolutionaryalgorithm
_version_	1718401757540777984

Modeling and parameter identification of seated human body with the reference vector guided evolutionary algorithm

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