{"title":"基于无侧偏角模型匹配方法的主动前转向控制器设计","authors":"Mert Sever, M. S. Arslan","doi":"10.1109/CEIT.2018.8751855","DOIUrl":null,"url":null,"abstract":"A side slip angle free model matching controller (MMC) is designed to improve vehicle yaw stability by active front steering. Optimization of controller gains is specified by a classical LQR problem. Additionally, LQR controller gains are structured to enable side slip angle free design. Design of an LQR having a structured controller gain is formulated as a convex optimization problem subject to linear matrix inequalities (LMIs) constraints. The proposed controller is designed with an augmented state space model including a linear bicycle model and model matching error dynamics. Superiority of the proposed controller is shown by numerically comparing with a classical full state feedback LQR. In order to obtain realistic results; a three-degrees-of-freedom nonlinear vehicle model is used. The nonlinear vehicle model is composed of lateral, yaw and longitudinal motions with the well-known Magic Formula tire model. Simulation results show that the proposed structured MMC provides very compatible performance with full state feedback LQR design.","PeriodicalId":357613,"journal":{"name":"2018 6th International Conference on Control Engineering & Information Technology (CEIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Active Front Steering Controller Design with Side Slip Angle Free Model Matching Approach\",\"authors\":\"Mert Sever, M. S. Arslan\",\"doi\":\"10.1109/CEIT.2018.8751855\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A side slip angle free model matching controller (MMC) is designed to improve vehicle yaw stability by active front steering. Optimization of controller gains is specified by a classical LQR problem. Additionally, LQR controller gains are structured to enable side slip angle free design. Design of an LQR having a structured controller gain is formulated as a convex optimization problem subject to linear matrix inequalities (LMIs) constraints. The proposed controller is designed with an augmented state space model including a linear bicycle model and model matching error dynamics. Superiority of the proposed controller is shown by numerically comparing with a classical full state feedback LQR. In order to obtain realistic results; a three-degrees-of-freedom nonlinear vehicle model is used. The nonlinear vehicle model is composed of lateral, yaw and longitudinal motions with the well-known Magic Formula tire model. Simulation results show that the proposed structured MMC provides very compatible performance with full state feedback LQR design.\",\"PeriodicalId\":357613,\"journal\":{\"name\":\"2018 6th International Conference on Control Engineering & Information Technology (CEIT)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 6th International Conference on Control Engineering & Information Technology (CEIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEIT.2018.8751855\",\"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 6th International Conference on Control Engineering & Information Technology (CEIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEIT.2018.8751855","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Active Front Steering Controller Design with Side Slip Angle Free Model Matching Approach
A side slip angle free model matching controller (MMC) is designed to improve vehicle yaw stability by active front steering. Optimization of controller gains is specified by a classical LQR problem. Additionally, LQR controller gains are structured to enable side slip angle free design. Design of an LQR having a structured controller gain is formulated as a convex optimization problem subject to linear matrix inequalities (LMIs) constraints. The proposed controller is designed with an augmented state space model including a linear bicycle model and model matching error dynamics. Superiority of the proposed controller is shown by numerically comparing with a classical full state feedback LQR. In order to obtain realistic results; a three-degrees-of-freedom nonlinear vehicle model is used. The nonlinear vehicle model is composed of lateral, yaw and longitudinal motions with the well-known Magic Formula tire model. Simulation results show that the proposed structured MMC provides very compatible performance with full state feedback LQR design.