An efficient nonlinear explicit model predictive control to regulate blood glucose in type-1 diabetic patient under parametric uncertainties. (January 2022) - Acharya, Debasis; Das, Dushmanta Kumar
Highlights: Nonlinear explicit model predictive control for blood glucose regulation. The control signal is minimized to avoid actuator saturation problem. It performs efficiently for different Virtual T1DM patients under parametric changes. Chance of hyperglycemia and hypoglycemia is avoided. Abstract: With the aim of regulating plasma glucose level in a type-1 diabetic patient, a nonlinear explicit model predictive control (NEMPC) is developed. The computational complexity in analytical method due to iterative process can be avoided by NEMPC. The amplitude of control signal defines the exogenous insulin infusion rate. Therefore, it should be taken a special care such that physiological needs can be satisfied. The control signal is also minimized to avoid physical hazard due to oscillation. The cost of actuator is also minimized as small size actuator can care the control signal generated by the proposed method. Thus, a cost effective control scheme is suggested by using nonlinear EMPC to regulate blood glucose level in type-1 diabetic patient. Different types of cases with nominal parameters, uncertainties in parameter and disturbances, uncertainties in states of initial condition are examined using the proposed method. The robustness of the proposed control logic is also checked with control variability analysis. Different risk factor of hyperglycemia and hypoglycemia is also verified. The simulation results shows the effectiveness of proposed control scheme for regulating glucose concentration in type-1 diabetic patients. An improvement of settling the glucose level from hyperglycemia to basal level within 120 min avoiding chance of hypoglycemia shows the effectiveness of proposed method than existing one.