{"title":"Identification and control of four-wheel-steering vehicles based on neural network","authors":"Lu Qiang, Wang Huiyi, Guo Kong-hui","doi":"10.1109/IVEC.1999.830677","DOIUrl":null,"url":null,"abstract":"Vehicle dynamics are influenced by various nonlinear factors, such as tire characteristics, road conditions, etc. Hence, it is difficult to represent the vehicle dynamics by means of a two-degrees-of-freedom linear model perfectly. This paper presents a new four-wheel-steering (4WS) control system with a neural network that has the abilities of nonlinear modeling and control. A vehicle model of the RBF network is identified from the vehicle dynamics firstly. Next, the authors design a radial basis function (RBF) network controller with this vehicle model of the RBF network. The effectiveness of the proposed method is demonstrated with computer simulations.","PeriodicalId":191336,"journal":{"name":"Proceedings of the IEEE International Vehicle Electronics Conference (IVEC'99) (Cat. No.99EX257)","volume":"181 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the IEEE International Vehicle Electronics Conference (IVEC'99) (Cat. No.99EX257)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVEC.1999.830677","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Vehicle dynamics are influenced by various nonlinear factors, such as tire characteristics, road conditions, etc. Hence, it is difficult to represent the vehicle dynamics by means of a two-degrees-of-freedom linear model perfectly. This paper presents a new four-wheel-steering (4WS) control system with a neural network that has the abilities of nonlinear modeling and control. A vehicle model of the RBF network is identified from the vehicle dynamics firstly. Next, the authors design a radial basis function (RBF) network controller with this vehicle model of the RBF network. The effectiveness of the proposed method is demonstrated with computer simulations.