Fuzzy model‐based multi‐objective dynamic programming with modified particle swarm optimization approach for the balance control of bicycle robot

Abstract Existing studies for the balance control of unmanned bicycle robots only consider constant forward velocity and a single optimal objective that cannot be applied to the complex motion situation. To balance the bicycle robot with time‐varying forward velocity, only with the steering actuator...

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Autores principales:	Yiyong Sun, Haotian Zhao, Zhang Chen, Xudong Zheng, Mingguo Zhao, Bin Liang
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2022
Materias:	Control engineering systems. Automatic machinery (General) TJ212-225
Acceso en línea:	https://doaj.org/article/c9d71d23727c42f4adf423979b66fdff
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spelling	oai:doaj.org-article:c9d71d23727c42f4adf423979b66fdff2021-12-02T15:00:29ZFuzzy model‐based multi‐objective dynamic programming with modified particle swarm optimization approach for the balance control of bicycle robot1751-86521751-864410.1049/cth2.12199https://doaj.org/article/c9d71d23727c42f4adf423979b66fdff2022-01-01T00:00:00Zhttps://doi.org/10.1049/cth2.12199https://doaj.org/toc/1751-8644https://doaj.org/toc/1751-8652Abstract Existing studies for the balance control of unmanned bicycle robots only consider constant forward velocity and a single optimal objective that cannot be applied to the complex motion situation. To balance the bicycle robot with time‐varying forward velocity, only with the steering actuator, the multiple objective optimal balance control issue is studied here. A fuzzy state‐space model under different forward velocities is firstly offered based on the non‐linear Euler–Lagrange model. Based on this, a closed‐loop equation under a fuzzy controller is verified. To regulate the feedback gain of the fuzzy controller, a modified particle swarm optimization (MPSO) algorithm with two stages is proposed. In the MPSO's second stage, a novel objective fitness function, consisting of multiple objectives and combining the conventional Hurwitz stability analysis criterium, is designed. Procedures for the MPSO dynamic programming approach are presented. By two examples, the efficiency of the MPSO algorithm, for time‐varying and time‐constant velocity situations, and faster capacity for iteration convergence, are examined.Yiyong SunHaotian ZhaoZhang ChenXudong ZhengMingguo ZhaoBin LiangWileyarticleControl engineering systems. Automatic machinery (General)TJ212-225ENIET Control Theory & Applications, Vol 16, Iss 1, Pp 7-19 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Control engineering systems. Automatic machinery (General) TJ212-225
spellingShingle	Control engineering systems. Automatic machinery (General) TJ212-225 Yiyong Sun Haotian Zhao Zhang Chen Xudong Zheng Mingguo Zhao Bin Liang Fuzzy model‐based multi‐objective dynamic programming with modified particle swarm optimization approach for the balance control of bicycle robot
description	Abstract Existing studies for the balance control of unmanned bicycle robots only consider constant forward velocity and a single optimal objective that cannot be applied to the complex motion situation. To balance the bicycle robot with time‐varying forward velocity, only with the steering actuator, the multiple objective optimal balance control issue is studied here. A fuzzy state‐space model under different forward velocities is firstly offered based on the non‐linear Euler–Lagrange model. Based on this, a closed‐loop equation under a fuzzy controller is verified. To regulate the feedback gain of the fuzzy controller, a modified particle swarm optimization (MPSO) algorithm with two stages is proposed. In the MPSO's second stage, a novel objective fitness function, consisting of multiple objectives and combining the conventional Hurwitz stability analysis criterium, is designed. Procedures for the MPSO dynamic programming approach are presented. By two examples, the efficiency of the MPSO algorithm, for time‐varying and time‐constant velocity situations, and faster capacity for iteration convergence, are examined.
format	article
author	Yiyong Sun Haotian Zhao Zhang Chen Xudong Zheng Mingguo Zhao Bin Liang
author_facet	Yiyong Sun Haotian Zhao Zhang Chen Xudong Zheng Mingguo Zhao Bin Liang
author_sort	Yiyong Sun
title	Fuzzy model‐based multi‐objective dynamic programming with modified particle swarm optimization approach for the balance control of bicycle robot
title_short	Fuzzy model‐based multi‐objective dynamic programming with modified particle swarm optimization approach for the balance control of bicycle robot
title_full	Fuzzy model‐based multi‐objective dynamic programming with modified particle swarm optimization approach for the balance control of bicycle robot
title_fullStr	Fuzzy model‐based multi‐objective dynamic programming with modified particle swarm optimization approach for the balance control of bicycle robot
title_full_unstemmed	Fuzzy model‐based multi‐objective dynamic programming with modified particle swarm optimization approach for the balance control of bicycle robot
title_sort	fuzzy model‐based multi‐objective dynamic programming with modified particle swarm optimization approach for the balance control of bicycle robot
publisher	Wiley
publishDate	2022
url	https://doaj.org/article/c9d71d23727c42f4adf423979b66fdff
work_keys_str_mv	AT yiyongsun fuzzymodelbasedmultiobjectivedynamicprogrammingwithmodifiedparticleswarmoptimizationapproachforthebalancecontrolofbicyclerobot AT haotianzhao fuzzymodelbasedmultiobjectivedynamicprogrammingwithmodifiedparticleswarmoptimizationapproachforthebalancecontrolofbicyclerobot AT zhangchen fuzzymodelbasedmultiobjectivedynamicprogrammingwithmodifiedparticleswarmoptimizationapproachforthebalancecontrolofbicyclerobot AT xudongzheng fuzzymodelbasedmultiobjectivedynamicprogrammingwithmodifiedparticleswarmoptimizationapproachforthebalancecontrolofbicyclerobot AT mingguozhao fuzzymodelbasedmultiobjectivedynamicprogrammingwithmodifiedparticleswarmoptimizationapproachforthebalancecontrolofbicyclerobot AT binliang fuzzymodelbasedmultiobjectivedynamicprogrammingwithmodifiedparticleswarmoptimizationapproachforthebalancecontrolofbicyclerobot
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Fuzzy model‐based multi‐objective dynamic programming with modified particle swarm optimization approach for the balance control of bicycle robot

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