Hypo/Hyperglycemic Constrained Design of IV Insulin Control for Type 1 Diabetic Patients With Meal and Initial Condition Uncertainties Using Sequential Quadratic Programming
{"title":"Hypo/Hyperglycemic Constrained Design of IV Insulin Control for Type 1 Diabetic Patients With Meal and Initial Condition Uncertainties Using Sequential Quadratic Programming","authors":"S. Nandi, T. Singh","doi":"10.1115/IMECE2018-87742","DOIUrl":null,"url":null,"abstract":"The focus of this paper is on the development of an open loop controller for type 1 diabetic patients which is robust to meal and initial condition uncertainties in the presence of hypo- and hyperglycemic constraints. Bernstein polynomials are used to parametrize the evolving uncertain blood-glucose. The unique bounding properties of these polynomials are then used to enforce the desired glycemic constraints. A convex optimization problem is posed in the perturbation space of the model and is solved repeatedly to sequentially converge on a sub-optimal solution. The proposed approach is demonstrated on the classic Bergman model for Type 1 diabetic patients.","PeriodicalId":332737,"journal":{"name":"Volume 3: Biomedical and Biotechnology Engineering","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Volume 3: Biomedical and Biotechnology Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/IMECE2018-87742","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The focus of this paper is on the development of an open loop controller for type 1 diabetic patients which is robust to meal and initial condition uncertainties in the presence of hypo- and hyperglycemic constraints. Bernstein polynomials are used to parametrize the evolving uncertain blood-glucose. The unique bounding properties of these polynomials are then used to enforce the desired glycemic constraints. A convex optimization problem is posed in the perturbation space of the model and is solved repeatedly to sequentially converge on a sub-optimal solution. The proposed approach is demonstrated on the classic Bergman model for Type 1 diabetic patients.