{"title":"用粒子群优化增强移动机器人系统模糊控制响应","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":"{\"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}","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}
Enhancement of The Fuzzy Control Response with Particle Swarm Optimization in Mobile Robot System
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.
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