{"title":"基于概率模型的网络化自动化系统控制质量优化","authors":"J. Greifeneder, Georg Frey","doi":"10.1109/ETFA.2006.355428","DOIUrl":null,"url":null,"abstract":"New technological trends lead to the increasing use of network technologies in automation. Especially the Ethernet with TCP/IP and wireless networks find growing acceptance. The resulting networked automation systems (NAS) display properties such as stochastic delays and information loss, which are not known in classical automation structures. When control quality is to be assessed, these properties have to be determined. In this paper, the determination is achieved in a modeling approach based on probabilistic timed automata (PTA). The derived models allow the analysis of delays using probabilistic model checking (PMC). A case study illustrates how the results of the analysis can be applied to increase the product quality in a manufacturing system controlled by an NAS.","PeriodicalId":431393,"journal":{"name":"2006 IEEE Conference on Emerging Technologies and Factory Automation","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"Optimizing Quality of Control in Networked Automation Systems using Probabilistic Models\",\"authors\":\"J. Greifeneder, Georg Frey\",\"doi\":\"10.1109/ETFA.2006.355428\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"New technological trends lead to the increasing use of network technologies in automation. Especially the Ethernet with TCP/IP and wireless networks find growing acceptance. The resulting networked automation systems (NAS) display properties such as stochastic delays and information loss, which are not known in classical automation structures. When control quality is to be assessed, these properties have to be determined. In this paper, the determination is achieved in a modeling approach based on probabilistic timed automata (PTA). The derived models allow the analysis of delays using probabilistic model checking (PMC). A case study illustrates how the results of the analysis can be applied to increase the product quality in a manufacturing system controlled by an NAS.\",\"PeriodicalId\":431393,\"journal\":{\"name\":\"2006 IEEE Conference on Emerging Technologies and Factory Automation\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE Conference on Emerging Technologies and Factory Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ETFA.2006.355428\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE Conference on Emerging Technologies and Factory Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETFA.2006.355428","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimizing Quality of Control in Networked Automation Systems using Probabilistic Models
New technological trends lead to the increasing use of network technologies in automation. Especially the Ethernet with TCP/IP and wireless networks find growing acceptance. The resulting networked automation systems (NAS) display properties such as stochastic delays and information loss, which are not known in classical automation structures. When control quality is to be assessed, these properties have to be determined. In this paper, the determination is achieved in a modeling approach based on probabilistic timed automata (PTA). The derived models allow the analysis of delays using probabilistic model checking (PMC). A case study illustrates how the results of the analysis can be applied to increase the product quality in a manufacturing system controlled by an NAS.