{"title":"基于蚁群优化的血糖调节模型预测控制","authors":"Y. Ho, Binh P. Nguyen, C. Chui","doi":"10.1145/2350716.2350749","DOIUrl":null,"url":null,"abstract":"This paper presents an adaptation of the Ant System method to find the optimal control input for blood glucose regulation using Model Predictive Control (MPC). The Ant System optimization method was implemented to solve a linear MPC problem and performance was compared with the interior point method for optimization. The Ant System was found to perform well for the linear MPC problem and has the advantage over the interior point method as it can extended for use with non-linear MPC problems.","PeriodicalId":208300,"journal":{"name":"Proceedings of the 3rd Symposium on Information and Communication Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Ant colony optimization for model predictive control for blood glucose regulation\",\"authors\":\"Y. Ho, Binh P. Nguyen, C. Chui\",\"doi\":\"10.1145/2350716.2350749\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an adaptation of the Ant System method to find the optimal control input for blood glucose regulation using Model Predictive Control (MPC). The Ant System optimization method was implemented to solve a linear MPC problem and performance was compared with the interior point method for optimization. The Ant System was found to perform well for the linear MPC problem and has the advantage over the interior point method as it can extended for use with non-linear MPC problems.\",\"PeriodicalId\":208300,\"journal\":{\"name\":\"Proceedings of the 3rd Symposium on Information and Communication Technology\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 3rd Symposium on Information and Communication Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2350716.2350749\",\"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 3rd Symposium on Information and Communication Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2350716.2350749","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Ant colony optimization for model predictive control for blood glucose regulation
This paper presents an adaptation of the Ant System method to find the optimal control input for blood glucose regulation using Model Predictive Control (MPC). The Ant System optimization method was implemented to solve a linear MPC problem and performance was compared with the interior point method for optimization. The Ant System was found to perform well for the linear MPC problem and has the advantage over the interior point method as it can extended for use with non-linear MPC problems.