Dynamic Model Development And Receding Horizon Control Of Blood Glucose Concentration

Biological systems usually consist of large number of components and involve processes ata variety of spatial, temporal and biological scales. This study presents a framework formodel identification and the use of Global Sensitivity Analysis (GSA) in systems biologymodelling and shows how the information content of clinical data from Short InsulinTolerance Test (SITT) can be handled by optimal model-based estimation techniques. Thegoal is to identify dynamic model of type II diabetes and estimate a set of parameters of themodel with greater accuracy and precision. Based on the SITT data, the blood glucosedynamic model was identified as a system of linear differential equation with constantcoefficients (parameters). The sensitivity of the parameters was tested using a novel GSAbased approach, and Derivative-Based Global Sensitivity Measures (DGSM). The proposedapproach was implemented in SensSB (a Matlab based toolbox). For the purpose ofcomparison, the sensitivity of the model was also tested using Sobol’s method and a localapproach. The results have shown that the model is less sensitive to the third parameter ( )and the model fits the SITT data satisfactorily. Subsequently, a control strategy calledreceding horizon control was investigated to regulate blood glucose concentration under themodel predictive control framework. Two forms of receding horizon control strategy (fixedendand moving-end) were proposed and applied to the dynamic model to maintain bloodglucose concentration. Different disturbance scenarios were generated to evaluate theperformance of the two strategies in terms of its efficiency to handle disturbances. Thecontrol strategies successfully addressed the issues of the input/external disturbanceconsidered for the patients in a virtual situations which maintain blood glucose level at 80.06mg/dL.2 K

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