{"title":"Robust controller design for an autonomous underwater vehicle","authors":"A. Wadood, S. Anavatti, O. Hassanein","doi":"10.1109/ICACI.2017.7974515","DOIUrl":null,"url":null,"abstract":"The control of Autonomous Underwater Vehicles (AUVs) is challenging because of its highly nonlinear and time-varying dynamics Fuzzy logic has the ability to model any nonlinear system. Recently/interval Type-2 Fuzzy logic (IT2FL) has gained interest due to its inherent ability to handle uncertainties. The purpose of this study is to employ IT2FLC for the control of an AUV Simulation experiments have been carried out. Results indicate that Interval Type-2 Fuzzy Logic Control (IT2FLC) has superior performance than Type-1 Fuzzy Logic Control in the presence of noise and parameter variations.","PeriodicalId":260701,"journal":{"name":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACI.2017.7974515","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
The control of Autonomous Underwater Vehicles (AUVs) is challenging because of its highly nonlinear and time-varying dynamics Fuzzy logic has the ability to model any nonlinear system. Recently/interval Type-2 Fuzzy logic (IT2FL) has gained interest due to its inherent ability to handle uncertainties. The purpose of this study is to employ IT2FLC for the control of an AUV Simulation experiments have been carried out. Results indicate that Interval Type-2 Fuzzy Logic Control (IT2FLC) has superior performance than Type-1 Fuzzy Logic Control in the presence of noise and parameter variations.
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