Multi Objective Nonlinear Model Predictive Control of Diabetes Models Considering the Effects of Insulin and Exercise

Rigorous multiobjective nonlinear model predictive control on the diabetes model incorporating single and multiple control strategies. The amount of glucose is minimized with the Bergman model considering the effects of insulin and exercise. The optimization language pyomo is used in conjunction with the state-of-the-art global optimization solvers IPOPT and Baron. Pareto surfaces are generated. When some optimal control profiles were found to exhibit sharp spikes, an activation factor involving the hyperbolic tangent function was used. It is observed that a greater amount of glucose minimization is achieved when more control procedures were incorporated. This demonstrates that it is more beneficial to use multiple control strategies