Model Predictive Control with Invariant Sets in Artificial Pancreas for Type 1 Diabetes Mellitus

Abstract: This thesis deals with Model Predictive Control (MPC) for artificial pancreas for Type 1 Diabetes Mellitus patients. A control strategy exploiting invariant sets in MPC for blood glucose level control is developed, to the authors knowledge for the first time. The work includes various types of invariant sets relevant for the artificial pancreas problem, and different ways to incorporate them into the MPC strategy. The work is an extension to the zone MPC controller for artificial pancreas developed at University of California Santa Barbara and Sansum Diabetes Research Institute. The evaluation of the proposed control strategy is done in silico in the U.S. Food and Drug Administration approved metabolic simulator. The trials show some promising results in terms of more rapid meal responses and decreased variability between the subjects than the zone MPC. An attempt to robust control employing invariant sets proved to be less promising in the evaluations. The results indicate that the direct application of known robust control techniques is not appropriate, and that more appropriate robust control techniques must be searched for, or developed, more specific to the artificial pancreas control. Altogether, this thesis pinpoints a possible future direction of artificial pancreas control design, with MPC based on invariant sets.