{"title":"厌氧消化过程设定点跟踪中一种更有效的迭代学习控制","authors":"Xiangjie Liu, Huimin Xu, Xuedong Zhang","doi":"10.1109/IHMSC.2015.136","DOIUrl":null,"url":null,"abstract":"To improve accuracy and efficiency of the set value tracking in anaerobic digestion process for wastewater treatment, a fast and high accurate ILC algorithm-closed loop iterative learning control is proposed. The introduced ILC utilizes m-accretive mapping theory to ensure the uniqueness of desired control input and a rigorous mathematical proof guarantees convergence of tracking error, at the same time the effectiveness of the proposed method is presented by simulations.","PeriodicalId":6592,"journal":{"name":"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics","volume":"107 1","pages":"468-471"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A More Efficient Iterative Learning Control for Anaerobic Digestion Process Set Point Tracking\",\"authors\":\"Xiangjie Liu, Huimin Xu, Xuedong Zhang\",\"doi\":\"10.1109/IHMSC.2015.136\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To improve accuracy and efficiency of the set value tracking in anaerobic digestion process for wastewater treatment, a fast and high accurate ILC algorithm-closed loop iterative learning control is proposed. The introduced ILC utilizes m-accretive mapping theory to ensure the uniqueness of desired control input and a rigorous mathematical proof guarantees convergence of tracking error, at the same time the effectiveness of the proposed method is presented by simulations.\",\"PeriodicalId\":6592,\"journal\":{\"name\":\"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics\",\"volume\":\"107 1\",\"pages\":\"468-471\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IHMSC.2015.136\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IHMSC.2015.136","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A More Efficient Iterative Learning Control for Anaerobic Digestion Process Set Point Tracking
To improve accuracy and efficiency of the set value tracking in anaerobic digestion process for wastewater treatment, a fast and high accurate ILC algorithm-closed loop iterative learning control is proposed. The introduced ILC utilizes m-accretive mapping theory to ensure the uniqueness of desired control input and a rigorous mathematical proof guarantees convergence of tracking error, at the same time the effectiveness of the proposed method is presented by simulations.
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