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In this work, a low-order model designed for glucose regulation in Type 1 Diabetes Mellitus (T1DM) is obtained from the UVA/Padova metabolic simulator. It captures not only the nonlinear behavior of the glucose-insulin system, but also intra-patient variations related to daily insulin sensitivity ($\mathrm{S_{I}}$) changes. To overcome the large inter-subject variability, the model can also be personalized based on a priori patient information. The structure is amenable for linear parameter varying (LPV) controller design, and represents the dynamics from the subcutaneous insulin input to the subcutaneous glucose output. The efficacy of this model is evaluated in comparison with a previous control-oriented model which in turn is an improvement of previous models. Both models are compared in terms of their open- and closed-loop differences with respect to the UVA/Padova model. The proposed model outperforms previous T1DM control-oriented models, which could potentially lead to more robust and reliable controllers for glycemia regulation. [ABSTRACT FROM AUTHOR]

• Based on a low-order control-oriented model an LPV model-set is calculated. • The LPV model set is computed by an (in)validation procedure. • A robust switched-LPV controller based on this set and another one based on the nominal LPV model are designed. • Simulations show that the robust controller can take care of different (dynamic, parametric) uncertainties, whereas the nominal controller cannot. The purpose of this work is to compute a linear parameter-varying (LPV) model set that describes the insulin-glucose dynamics in type 1 diabetes (T1D). This set includes a nominal LPV model and dynamic uncertainty and is amenable to controller design. The nominal model is an LPV control-oriented model previously published by the authors that is (in)validated in this work against the UVA/Padova metabolic simulator. The result is a set of models that is used to design a switched LPV robust controller to account for nonlinearities and variations in insulin sensitivity (S I). Closed-loop responses obtained with the robust controller and a nominal one are compared. Results illustrate the convenience of including robust strategies in designing control laws for an artificial pancreas (AP). [ABSTRACT FROM AUTHOR]

Type-1 diabetes mellitus (T1DM) is a challenging disorder which essentially involves regulation of the glucose levels to avoid hyperglycemia as well as hypoglycemia. For this purpose, this research paper proposes and develops control algorithms using an intelligent predictive control model, which is based on a UVA/Padova metabolic simulator. The primary objective of the designed control laws is to provide an automatic blood glucose control in insulin-dependent patients so as to improve their life quality and to reduce the need of an extremely demanding self-management plan. Various linear and nonlinear control algorithms have been explored and implemented on the estimated model. Linear techniques include the Proportional Integral Derivative (PID) and Linear Quadratic Regulator (LQR), and nonlinear control strategy includes the Sliding Mode Control (SMC), which are implemented in this research work for continuous monitoring of glucose levels. Performance comparison based on simulation results demonstrated that SMC proved to be most efficient in terms of regulating glucose profile to a reference level of 70 mg/dL compared to the classical linear techniques. A brief comparison is presented between the linear techniques (PID and LQR), and nonlinear technique (SMC) for analysis purposes proving the efficacy of the design. [ABSTRACT FROM AUTHOR]