This thesis presents different control strategies, for the closed-loop artificial pancreas, which are based on Model Predictive Control (MPC) and Sliding Mode Control (SMC). Multiple MPC with linear models and gain scheduling, and SMC with linear and nonlinear models, have been developed. The proposed control strategies combine more than one linear/nonlinear control and modeling approaches in one structure. The main idea behind such combined approaches is to make use of the virtues of each approach while reducing the effects of their drawbacks. The control strategies have been tested and validated in simulations (in-silico validation). For the in-silico testing, two mathematical models have been used, simulating patients with Type 1 Diabetes Mellitus. The control strategies are tested in different conditions, such as the presence of meal disturbance and patient variability
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