Enhancement of The Fuzzy Control Response with Particle Swarm Optimization in Mobile Robot System

S. Nurmaini, Febrina Setianingsih
{"title":"Enhancement of The Fuzzy Control Response with Particle Swarm Optimization in Mobile Robot System","authors":"S. Nurmaini, Febrina Setianingsih","doi":"10.1109/ICECOS.2018.8605221","DOIUrl":null,"url":null,"abstract":"Membership functions (MFs) play a crucial role in Fuzzy Logic based decision-making systems. However, in the fuzzy logic design, to select the value of MFs is difficult. Such process always using a trial and error way based on the linguistic from the expert and some works resulting in poor response. Hence, the selection of optimal MFs is desirable. In this research, the optimization method based on Particle Swarm Optimization (PSO) algorithm is applied to tuning the fuzzy membership functions. The method is implemented to control the position and orientation DDMR. By using such method, the fuzzy control produces good response in terms of fast rise time, minimum maximum peak overshoot and fast time to reach the steady-state condition.","PeriodicalId":149318,"journal":{"name":"2018 International Conference on Electrical Engineering and Computer Science (ICECOS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Electrical Engineering and Computer Science (ICECOS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICECOS.2018.8605221","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

Membership functions (MFs) play a crucial role in Fuzzy Logic based decision-making systems. However, in the fuzzy logic design, to select the value of MFs is difficult. Such process always using a trial and error way based on the linguistic from the expert and some works resulting in poor response. Hence, the selection of optimal MFs is desirable. In this research, the optimization method based on Particle Swarm Optimization (PSO) algorithm is applied to tuning the fuzzy membership functions. The method is implemented to control the position and orientation DDMR. By using such method, the fuzzy control produces good response in terms of fast rise time, minimum maximum peak overshoot and fast time to reach the steady-state condition.
用粒子群优化增强移动机器人系统模糊控制响应
隶属函数在模糊逻辑决策系统中起着至关重要的作用。然而,在模糊逻辑设计中,mf值的选取是一个难点。这种过程总是采用基于专家语言的试错方式,一些作品的反应不佳。因此,选择最优MFs是可取的。在本研究中,采用基于粒子群优化算法的优化方法对模糊隶属度函数进行调优。实现了对DDMR位置和方向的控制。采用该方法,模糊控制在上升时间快、最大峰值超调小、达到稳态时间快等方面具有良好的响应效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信