{"title":"Optimized Backstepping Fuzzy Control for Nonholonomic Mobile Robot","authors":"A. F. Amer, E. Sallam, I. Sultan","doi":"10.1109/ICCTA32607.2013.9529703","DOIUrl":null,"url":null,"abstract":"This paper proposes a tracking control method for nonholonomic mobile robots (NMRs) by using an optimized backstepping fuzzy control. Unlike previous backstepping controllers for wheeled mobile robots, an optimized backstepping fuzzy control structure is based on a backstepping technique to ensure stabilization of the mobile robots position and orientation around the desired trajectory, the kinematics and dynamics of the mobile robot are considered, the parameters of the nonlinear controller are optimized by using genetic algorithm to get accurate values, also the scaling factor of the fuzzy controller is optimized. In addition, numerical simulations for various reference trajectories show the validity of the proposed scheme.","PeriodicalId":405465,"journal":{"name":"2013 23rd International Conference on Computer Theory and Applications (ICCTA)","volume":"276 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 23rd International Conference on Computer Theory and Applications (ICCTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCTA32607.2013.9529703","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 proposes a tracking control method for nonholonomic mobile robots (NMRs) by using an optimized backstepping fuzzy control. Unlike previous backstepping controllers for wheeled mobile robots, an optimized backstepping fuzzy control structure is based on a backstepping technique to ensure stabilization of the mobile robots position and orientation around the desired trajectory, the kinematics and dynamics of the mobile robot are considered, the parameters of the nonlinear controller are optimized by using genetic algorithm to get accurate values, also the scaling factor of the fuzzy controller is optimized. In addition, numerical simulations for various reference trajectories show the validity of the proposed scheme.