{"title":"高速公路交通密度控制的迭代学习控制方法","authors":"Z. Hou, Jian-xin Xu","doi":"10.1109/ITSC.2003.1252652","DOIUrl":null,"url":null,"abstract":"In this paper, an iterative learning control scheme is developed to the traffic density control in a macroscopic level freeway environment. With rigorous analysis, the proposed intelligent control scheme guarantees the asymptotic convergence of the traffic density to the desired one. The control scheme is applied to a freeway model, and simulation results confirm the efficacy of the proposed approach.","PeriodicalId":123155,"journal":{"name":"Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"Freeway traffic density control using iterative learning control approach\",\"authors\":\"Z. Hou, Jian-xin Xu\",\"doi\":\"10.1109/ITSC.2003.1252652\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, an iterative learning control scheme is developed to the traffic density control in a macroscopic level freeway environment. With rigorous analysis, the proposed intelligent control scheme guarantees the asymptotic convergence of the traffic density to the desired one. The control scheme is applied to a freeway model, and simulation results confirm the efficacy of the proposed approach.\",\"PeriodicalId\":123155,\"journal\":{\"name\":\"Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-12-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITSC.2003.1252652\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITSC.2003.1252652","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Freeway traffic density control using iterative learning control approach
In this paper, an iterative learning control scheme is developed to the traffic density control in a macroscopic level freeway environment. With rigorous analysis, the proposed intelligent control scheme guarantees the asymptotic convergence of the traffic density to the desired one. The control scheme is applied to a freeway model, and simulation results confirm the efficacy of the proposed approach.
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