{"title":"Designing Adaptive Control Based on Bacteria Foraging Optimization","authors":"H. Elaydi, Ramzi J. Al Ghamri","doi":"10.1109/PICICT.2017.16","DOIUrl":null,"url":null,"abstract":"This paper presents a model reference adaptive control (MRAC) based on bacteria foraging optimization algorithm (BFOA) to control maglev model CE152 which is unstable nonlinear system. An adaptive controller is designed to keep the magnetic ball suspended in the air counteracting the weight of the object. A MRAC based on BFOA to control of this system is designed and simulated using Matlab/Simulink. The results are compared with Fuzzy Logic (FL) control, and Fuzzy Logic based Genetic Algorithm (GA) control. The proposed approach outperformed all other approaches in terms of overshoot, rise and settling time and steady state error.","PeriodicalId":259869,"journal":{"name":"2017 Palestinian International Conference on Information and Communication Technology (PICICT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Palestinian International Conference on Information and Communication Technology (PICICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PICICT.2017.16","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper presents a model reference adaptive control (MRAC) based on bacteria foraging optimization algorithm (BFOA) to control maglev model CE152 which is unstable nonlinear system. An adaptive controller is designed to keep the magnetic ball suspended in the air counteracting the weight of the object. A MRAC based on BFOA to control of this system is designed and simulated using Matlab/Simulink. The results are compared with Fuzzy Logic (FL) control, and Fuzzy Logic based Genetic Algorithm (GA) control. The proposed approach outperformed all other approaches in terms of overshoot, rise and settling time and steady state error.
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