{"title":"一种数据驱动的分数阶终端滑模控制方法","authors":"Mingdong Hou, Yinsong Wang","doi":"10.1109/DDCLS.2019.8908857","DOIUrl":null,"url":null,"abstract":"For a class of discrete-time nonlinear systems with disturbances, a data-driven discrete-time fractional-order terminal sliding mode control (DD-DFOTSMC) method is proposed in this paper. The algorithm is based on the compact form dynamic linearization (CFDL) technique, and the controller is designed based on the discrete terminal sliding mode technology and the Grünwald-Letnikov fractional-order definition. The parameter of the CFDL data model is called pseudo-partial derivative (PPD) and is estimated using merely I/O measurement data of the system. Theoretical analysis proves the stability of the proposed algorithm, and simulation studies demonstrate that the proposed method has higher precision and faster response speed. Finally, the effectiveness of the proposed method is validated through a continuous stirred tank reactor (CSTR) process.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"8 1","pages":"42-46"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Data Driven Fractional-order Terminal Sliding Mode Control Method\",\"authors\":\"Mingdong Hou, Yinsong Wang\",\"doi\":\"10.1109/DDCLS.2019.8908857\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For a class of discrete-time nonlinear systems with disturbances, a data-driven discrete-time fractional-order terminal sliding mode control (DD-DFOTSMC) method is proposed in this paper. The algorithm is based on the compact form dynamic linearization (CFDL) technique, and the controller is designed based on the discrete terminal sliding mode technology and the Grünwald-Letnikov fractional-order definition. The parameter of the CFDL data model is called pseudo-partial derivative (PPD) and is estimated using merely I/O measurement data of the system. Theoretical analysis proves the stability of the proposed algorithm, and simulation studies demonstrate that the proposed method has higher precision and faster response speed. Finally, the effectiveness of the proposed method is validated through a continuous stirred tank reactor (CSTR) process.\",\"PeriodicalId\":6699,\"journal\":{\"name\":\"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)\",\"volume\":\"8 1\",\"pages\":\"42-46\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DDCLS.2019.8908857\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DDCLS.2019.8908857","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Data Driven Fractional-order Terminal Sliding Mode Control Method
For a class of discrete-time nonlinear systems with disturbances, a data-driven discrete-time fractional-order terminal sliding mode control (DD-DFOTSMC) method is proposed in this paper. The algorithm is based on the compact form dynamic linearization (CFDL) technique, and the controller is designed based on the discrete terminal sliding mode technology and the Grünwald-Letnikov fractional-order definition. The parameter of the CFDL data model is called pseudo-partial derivative (PPD) and is estimated using merely I/O measurement data of the system. Theoretical analysis proves the stability of the proposed algorithm, and simulation studies demonstrate that the proposed method has higher precision and faster response speed. Finally, the effectiveness of the proposed method is validated through a continuous stirred tank reactor (CSTR) process.