Design and Implementation of the Dual-Hormone Artificial Pancreas in Animal Studies – A Model Predictive Control Approach with Intraperitoneal Hormone Injections

Abstract

In this thesis, the exploration of new ways to help people with Type 1 Diabetes better control their condition is described. In this research, our primary focus was on the feasibility and benefits of utilizing the intraperitoneal (IP) route for insulin and glucagon injections in Type 1 DiabetesMellitus (T1DM), chosen due to its significantly faster absorption and more rapid effects on glucose levels compared to the subcutaneous (SC) route. The core contribution of this research lies in the development of a fully automated dual hormone AP system and testing in various animal experiments. In the first major part of this work, a new model is introduced, designed with a minimal number of parameters and states, exclusively intended for control applications within dual-hormone AP systems. Demonstrating remarkable prediction accuracy in over 30 animal experiments, this model represents a significant advancement that has the potential to facilitate future advancements in diabetes management. Subsequently, an estimator based on the Moving Horizon Estimation (MHE) method is designed, incorporating embedded prior knowledge to effectively estimate non-measurable states of the model, as well as meals and exercises. The experimental evaluation showcases the high accuracy of the estimator, further validating its potential as a valuable tool in diabetes care future. The work proceeds with the development of an MPC-based controller, adeptly incorporating practical considerations. Extensively tested in both in vivo and in silico experiments, the controller demonstrates high performance, surpassing existing Hybrid Closed-Loop AP systems in the market. Importantly, the proposed controller does not necessitate the meals and exercise announcements, enhancing its user-friendliness and autonomy compared to the commercial devices which all require meal announcements. Beyond the primary research target, this study delves into various other areas within diabetes management. The investigation includes testing a two-layer PID controller scheme, developing a method to compensate for CGM sensor time lag, exploring sensor fusion techniques to enhance glucose measurements, and studying experimental design strategies to increase model parameter identification accuracy. The findings of this research contribute to the advancement of diabetes research, which in turn may result in advances in diabetes care. The proposed model, estimator, and controller collectively offer a comprehensive and efficient solution for achieving reliable glycemic control in T1DM patients with IP injections. Ultimately, this work represents a vital step forward in personalized care and opens new avenues for future research and technological innovations.