{"title":"Containment Maneuvering for a Class of Uncertain Nonlinear Systems Based on Concurrent Learning","authors":"Yibo Zhang, Dan Wang, Zhouhua Peng","doi":"10.1109/ICIST.2018.8426162","DOIUrl":null,"url":null,"abstract":"In this paper, we investigate a containment maneuvering problem for uncertain nonlinear systems in MIMO strict-feedback form. The outputs of followers are driven to converge to a convex hull spanned by multiple parameterized paths and path variables need to satisfy a given dynamic task. A containment maneuvering controller is proposed based on a modular design approach. First, an estimation module is developed based on an RBF network, and adaption laws are proposed based on a concurrent learning method. Then, a controller module is proposed based on a modified dynamic surface control method using a second-order nonlinear tracking differentiator. At last, a path update law is designed by using a distributed maneuvering error feedback. Input-to-state stability theory and cascade theory are utilized to analyze the stability of the closed-loop system. The proposed design is a distributed method and attains adaption without the persistent excitation condition.","PeriodicalId":331555,"journal":{"name":"2018 Eighth International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Eighth International Conference on Information Science and Technology (ICIST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIST.2018.8426162","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
In this paper, we investigate a containment maneuvering problem for uncertain nonlinear systems in MIMO strict-feedback form. The outputs of followers are driven to converge to a convex hull spanned by multiple parameterized paths and path variables need to satisfy a given dynamic task. A containment maneuvering controller is proposed based on a modular design approach. First, an estimation module is developed based on an RBF network, and adaption laws are proposed based on a concurrent learning method. Then, a controller module is proposed based on a modified dynamic surface control method using a second-order nonlinear tracking differentiator. At last, a path update law is designed by using a distributed maneuvering error feedback. Input-to-state stability theory and cascade theory are utilized to analyze the stability of the closed-loop system. The proposed design is a distributed method and attains adaption without the persistent excitation condition.