Multi-parametric model predictive control-based regulation of blood glucose in type 1 diabetics under unmeasured meal disturbances: a simulation study Online publication date: Thu, 16-Oct-2014
by Velswamy Kirubakaran; T.K. Radhakrishnan; Natarajan Sivakumaran
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 14, No. 2, 2014
Abstract: Closed-loop control of Blood Glucose Concentration (BGC) is performed using a model differencing-based multi-parametric model predictive controller. An empirical model derived from a virtual nominal patient is used in the design of the controller. A time varying hard constraint is imposed on the control action based on insulin onboard criterion. By varying the model parameters used in the Sorensen's patient model, 25 virtual patients are created. Varying meal disturbances over a period of five days, the Mean Square Error (MSE) of BGC's deviation from reference (100 mg/dL) is used to tune the controller. In-silico trials with simulated sensor noise (± 20 mg/dL range) show that the multi-parametric model predictive controller performs well under model uncertainties and also regulates the BGC within the safe limits (above 60 mg/dL) during multiple meal disturbances.
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