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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Series: | Advances in Mechanical Engineering |
Online Access: | https://doi.org/10.1177/1687814018755215 |
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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 |
work_keys_str_mv |
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