{"title":"疾病进化动力学的逆向工程组合疗法:一种<s:1>∞方法","authors":"Vanessa D. Jonsson, N. Matni, R. Murray","doi":"10.1109/CDC.2013.6760185","DOIUrl":null,"url":null,"abstract":"We propose a general algorithm for the systematic design of feedback strategies to stabilize the evolutionary dynamics of a generic disease model using an H∞ approach. We show that designing therapy concentrations can be cast as an H∞ state feedback synthesis problem, where the feedback gain is constrained to not only be strictly diagonal, but also that its diagonal elements satisfy an overdetermined set of linear equations. Leveraging recent results in positive systems, we develop an algorithm that always yields a stabilizing controller.","PeriodicalId":415568,"journal":{"name":"52nd IEEE Conference on Decision and Control","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"Reverse engineering combination therapies for evolutionary dynamics of disease: An ℌ∞ approach\",\"authors\":\"Vanessa D. Jonsson, N. Matni, R. Murray\",\"doi\":\"10.1109/CDC.2013.6760185\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a general algorithm for the systematic design of feedback strategies to stabilize the evolutionary dynamics of a generic disease model using an H∞ approach. We show that designing therapy concentrations can be cast as an H∞ state feedback synthesis problem, where the feedback gain is constrained to not only be strictly diagonal, but also that its diagonal elements satisfy an overdetermined set of linear equations. Leveraging recent results in positive systems, we develop an algorithm that always yields a stabilizing controller.\",\"PeriodicalId\":415568,\"journal\":{\"name\":\"52nd IEEE Conference on Decision and Control\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"52nd IEEE Conference on Decision and Control\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CDC.2013.6760185\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"52nd IEEE Conference on Decision and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC.2013.6760185","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Reverse engineering combination therapies for evolutionary dynamics of disease: An ℌ∞ approach
We propose a general algorithm for the systematic design of feedback strategies to stabilize the evolutionary dynamics of a generic disease model using an H∞ approach. We show that designing therapy concentrations can be cast as an H∞ state feedback synthesis problem, where the feedback gain is constrained to not only be strictly diagonal, but also that its diagonal elements satisfy an overdetermined set of linear equations. Leveraging recent results in positive systems, we develop an algorithm that always yields a stabilizing controller.