{"title":"全息制造控制中的干扰检测、恢复和预测","authors":"P. Leitão, J. Barbosa","doi":"10.1109/DIS.2006.27","DOIUrl":null,"url":null,"abstract":"Disturbance handling is a crucial issue in the development of intelligent and reconfigurable manufacturing control systems, supporting the fast and promptly response to the occurrence of unexpected disturbances. In holonic manufacturing control systems, the disturbance handling functions are distributed by the several autonomous control units. In this paper, a predictive disturbance handling approach is presented, transforming the traditional \"fail and recover\" practices into \"predict and prevent\" practices, allowing improving the control system performance","PeriodicalId":318812,"journal":{"name":"IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications (DIS'06)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Disturbance Detection, Recover and Prediction in Holonic Manufacturing Control\",\"authors\":\"P. Leitão, J. Barbosa\",\"doi\":\"10.1109/DIS.2006.27\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Disturbance handling is a crucial issue in the development of intelligent and reconfigurable manufacturing control systems, supporting the fast and promptly response to the occurrence of unexpected disturbances. In holonic manufacturing control systems, the disturbance handling functions are distributed by the several autonomous control units. In this paper, a predictive disturbance handling approach is presented, transforming the traditional \\\"fail and recover\\\" practices into \\\"predict and prevent\\\" practices, allowing improving the control system performance\",\"PeriodicalId\":318812,\"journal\":{\"name\":\"IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications (DIS'06)\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-06-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications (DIS'06)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DIS.2006.27\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications (DIS'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DIS.2006.27","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Disturbance Detection, Recover and Prediction in Holonic Manufacturing Control
Disturbance handling is a crucial issue in the development of intelligent and reconfigurable manufacturing control systems, supporting the fast and promptly response to the occurrence of unexpected disturbances. In holonic manufacturing control systems, the disturbance handling functions are distributed by the several autonomous control units. In this paper, a predictive disturbance handling approach is presented, transforming the traditional "fail and recover" practices into "predict and prevent" practices, allowing improving the control system performance
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