{"title":"一类非线性系统基于数据的自适应输出反馈跟踪控制","authors":"Ling Ren, Guoshan Zhang","doi":"10.1109/DDCLS.2018.8516058","DOIUrl":null,"url":null,"abstract":"In this paper, we propose an output feedback tracking control scheme for a class of continuous-time nonlinear systems without specific model. A radial basis function neural network (RBFNN) observer is designed to online estimate the unmeasured inner state variables only using the input and output data. Based on the designed RBFNN observer, a sliding mode controller is derived to guarantee that the system states follow the desired trajectories. Simulation results on an example show the effectiveness and tracking performance of the proposed scheme.","PeriodicalId":6565,"journal":{"name":"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"421 1","pages":"246-250"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Data-Based Adaptive Output Feedback Tracking Control for a Class of Nonlinear Systems\",\"authors\":\"Ling Ren, Guoshan Zhang\",\"doi\":\"10.1109/DDCLS.2018.8516058\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose an output feedback tracking control scheme for a class of continuous-time nonlinear systems without specific model. A radial basis function neural network (RBFNN) observer is designed to online estimate the unmeasured inner state variables only using the input and output data. Based on the designed RBFNN observer, a sliding mode controller is derived to guarantee that the system states follow the desired trajectories. Simulation results on an example show the effectiveness and tracking performance of the proposed scheme.\",\"PeriodicalId\":6565,\"journal\":{\"name\":\"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)\",\"volume\":\"421 1\",\"pages\":\"246-250\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DDCLS.2018.8516058\",\"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 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DDCLS.2018.8516058","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Data-Based Adaptive Output Feedback Tracking Control for a Class of Nonlinear Systems
In this paper, we propose an output feedback tracking control scheme for a class of continuous-time nonlinear systems without specific model. A radial basis function neural network (RBFNN) observer is designed to online estimate the unmeasured inner state variables only using the input and output data. Based on the designed RBFNN observer, a sliding mode controller is derived to guarantee that the system states follow the desired trajectories. Simulation results on an example show the effectiveness and tracking performance of the proposed scheme.
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