An application of the brain limbic system–based control to the electromechanical brake system

Recently, many mechanical components of vehicles are being replaced by electrical or electromechanical parts. This change leads to the reduced weight, higher fuel efficiency, and simpler diagnosis. In particular, a reliable and robust electromechanical brake control system has been focused. So, this...

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Main Authors: Changwon Kim, Yeesock Kim, Ohwon Kwon, Joonho Seo, Dongkyu Lee, Hak Yi
Format: Article
Language:English
Published: SAGE Publishing 2018-02-01
Series:Advances in Mechanical Engineering
Online Access:https://doi.org/10.1177/1687814018755215
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spelling doaj-db2b0b6e595642de8134c9be83e8520c2020-11-25T03:44:12ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402018-02-011010.1177/1687814018755215An application of the brain limbic system–based control to the electromechanical brake systemChangwon Kim0Yeesock Kim1Ohwon Kwon2Joonho Seo3Dongkyu Lee4Hak Yi5Daegu Research Center for Medical Devices and Rehabilitation Engineering, Korea Institute of Machinery & Materials, Daegu, KoreaGordon and Jill Bourns College of Engineering, California Baptist University, Riverside, CA, USADaegu Research Center for Medical Devices and Rehabilitation Engineering, Korea Institute of Machinery & Materials, Daegu, KoreaDaegu Research Center for Medical Devices and Rehabilitation Engineering, Korea Institute of Machinery & Materials, Daegu, KoreaDaegu Research Center for Medical Devices and Rehabilitation Engineering, Korea Institute of Machinery & Materials, Daegu, KoreaSchool of Mechanical Engineering, Kyungpook National University, Daegu, KoreaRecently, many mechanical components of vehicles are being replaced by electrical or electromechanical parts. This change leads to the reduced weight, higher fuel efficiency, and simpler diagnosis. In particular, a reliable and robust electromechanical brake control system has been focused. So, this article investigates that an application of brain limbic system–based control, a bio-inspired control strategy, to an electromechanical brake system is suitable for dynamic and uncertain driving conditions. To do this end, control parameters are optimized through genetic algorithm. Results show the effectiveness of the suggested control method in terms of control speed, reference tracking, and robustness to the disturbance.https://doi.org/10.1177/1687814018755215
collection DOAJ
language English
format Article
sources DOAJ
author Changwon Kim
Yeesock Kim
Ohwon Kwon
Joonho Seo
Dongkyu Lee
Hak Yi
spellingShingle Changwon Kim
Yeesock Kim
Ohwon Kwon
Joonho Seo
Dongkyu Lee
Hak Yi
An application of the brain limbic system–based control to the electromechanical brake system
Advances in Mechanical Engineering
author_facet Changwon Kim
Yeesock Kim
Ohwon Kwon
Joonho Seo
Dongkyu Lee
Hak Yi
author_sort Changwon Kim
title An application of the brain limbic system–based control to the electromechanical brake system
title_short An application of the brain limbic system–based control to the electromechanical brake system
title_full An application of the brain limbic system–based control to the electromechanical brake system
title_fullStr An application of the brain limbic system–based control to the electromechanical brake system
title_full_unstemmed An application of the brain limbic system–based control to the electromechanical brake system
title_sort application of the brain limbic system–based control to the electromechanical brake system
publisher SAGE Publishing
series Advances in Mechanical Engineering
issn 1687-8140
publishDate 2018-02-01
description Recently, many mechanical components of vehicles are being replaced by electrical or electromechanical parts. This change leads to the reduced weight, higher fuel efficiency, and simpler diagnosis. In particular, a reliable and robust electromechanical brake control system has been focused. So, this article investigates that an application of brain limbic system–based control, a bio-inspired control strategy, to an electromechanical brake system is suitable for dynamic and uncertain driving conditions. To do this end, control parameters are optimized through genetic algorithm. Results show the effectiveness of the suggested control method in terms of control speed, reference tracking, and robustness to the disturbance.
url https://doi.org/10.1177/1687814018755215
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