(242q) Predictive Control of Blood Glucose Concentration in Type I Diabetic Patients in Presence of Unmeasured Disturbances Using Identified Models

Control of Blood Glucose concentration in type-I diabetic patients in presence of meal disturbances has attracted the attention of many researchers in the recent past [1-4].  The success of the control strategies proposed in the literature depends on the accuracy of the model used in the control frame-work [4]. In this work, a data based model predictive control algorithm is developed to control the blood glucose concentration in the Type-I diabetic patients in the presence of meal disturbances under patient-model mismatch. A state space model with augmented states representing integrating type of disturbances is developed [5].  This augmented state space model is used for the future predictions and then in optimizing the future insulin infusion rate based on the previous blood glucose measurements and previous insulin infusion rates, using a Model predictive control framework.  The states along with the disturbances at each sampling instant are estimated using recursive form of Kalman filter.  Appropriate physical and physiological constraints are incorporated in the objective function of MPC to ensure feasible operating regime. Simulation studies are performed on three distinct patient models using  Simulink^(R). The input-output data required for model identification has been obtained from the perturbation studies on patient-1. The mathematical model developed is used in the State Estimation based Linear Model Predictive Control, which is employed on all three patients. The simulation results revealed that, the proposed control strategy is able to control the blood glucose concentration well within the acceptable limits in the presence of meal disturbances. It was also observed that performance of this strategy,  even when large patient-model mismatch along with unmeasured disturbances, is quite encouraging.  With the technological advancements in infusion pumps, in vivo glucose sensors and microprocessor chips [6] it is possible to incorporate this robust control algorithm to build a portable insulin infusion control system that ensures normoglycemia in type-I diabetic patients.

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 [ 4 ] R.S. Parker, F.J. Doyle, and N.A. Peppas, A Model-Based Algorithm for Blood Glucose Control in Type I Diabetic Patients, IEEE Transactions in Biomedical  Engineering, 46, 2, 148-157, 1999.

 [ 5 ] K.R. Muske, T.A. Badgwell, Disturbance modeling for offset-free linear model predictive control  Journal of Process Control , 12, pp. 617?632, 2002.

 [ 6 ] G.S. Wilson, R. Gifford, Biosensors for real-time in vivo measurements, Biosensors and Bioelectronics,  vol. 20(12), pp. 2388-2403,  2005.