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We propose a Kalman filter-based observer utilizing noisy remote compartment insulin measurements to estimate plasma insulin concentration. The design considers plant-model mismatch, sensor noise, as well as both uniform sampling intervals, mimicking infrequent continuous measurements, and non-uniform sampling intervals, mimicking infrequent on-demand measurements. The performance of the observer is demonstrated on ten in-silico subjects from the UVA/Padova simulator using real-life scenarios, including variability in sensor noise and variability in insulin pharmacokinetics. The proposed observer provides insight into the future use of insulin measurements for diabetes management.

Artificial pancreas (AP) systems are designed to automate glucose management for patients with type 1 diabetes. In this work, we propose an adaptive disturbance rejection control approach for AP systems to achieve safe and effective glucose regulation. The controller is built within the framework of active disturbance rejection control, but incorporates safety operation constraints, and glucose- and velocity-dependent parameter adaptation modules for the key control parameters. In silico performance comparison between the proposed controller and an adaptive zone model predictive controller (MPC) (Shi, Dassau, and Doyle III, 2019a) is conducted on the 10-adult cohort of the FDA-accepted UVA/Padova T1DM simulator. For both announced and unannounced meals, the controller achieves comparable glucose regulation performance in terms of mean glucose (134.9 mg/dL vs. 135.4 mg/dL, p

Clinical trials have demonstrated that zone model predictive control is an effective closed-loop blood glucose regulation method for people with type 1 diabetes (T1D). This paper presents a universal model-free optimization method to seek an optimal zone for T1D patients individually. A clinical glycemic risk index named relative regularized glycemic penalty index (rrGPI) is used as the cost function. The proposed method is based on extremum seeking control that uses only the rrGPI index, calculated from measurements by a continuous glucose monitor, to update a controller’s blood glucose target zone’s upper bound and lower bound simultaneously. The method proposed uses a decaying feedback gain and a vanishing dither signal to improve the extremum seeking controller’s robustness against various uncertainties. In silico trials suggest that the proposed method is able to converge to the personalized optimal zone in less than a week of adaptation. In a 30-day in silico trial, the time spent in the range [70,180] mg/dL is increased by about 3% and 2% for unannounced 60 gCHO (grams of carbohydrates) and 90 gCHO meals, respectively, compared to the zone [80,140] mg/dL employed in the authors’ current zone controller.

A novel Model Predictive Control (MPC) strategy for an artificial pancreas to treat people with type 1 diabetes mellitus is proposed and tested in-silico. The proposed controller has been designed to handle a large spectrum of meal sizes more effectively than the authors’ current strategy Hyperglycemia due to small meals is treated more assertively leading to increased time in euglycemia, whereas hyperglycemia due to large meals is treated more cautiously resulting in a markedly reduced risk of controller-induced hypoglycemia. This is achieved by replacing the purely quadratic cost function employed within the MPC optimization by a mixed linear-quadratic cost function. This simple modification results in significant leeway to design a controller that responds more effectively and safely to the dietary habits of the AP user, while maintaining the quadratic program structure of the resulting optimization problem. The proposed MPC strategy’s benefits are demonstrated by in-silico analysis using the Universities of Virginia/Padova US Food & Drug Administration accepted metabolic simulator.

The properties of an objective function are fundamental to the functionality of a model predictive controller (MPC). In automated glucose control, avoidance of hypo- and hyperglycemia introduces significant challenges in the design of this cost function. We present a new formulation of the cost function for an MPC based on clinical requirements, and validate the algorithm under in silico and advisory mode assessments. The proposed formulation exhibits significant improvements in avoiding hypoglycemia, compared to clinically validated controllers, and can also mitigate hyperglycemia, across a wide range of in silico scenarios as well as glucose responses seen in actual clinical settings.

The accuracy of continuous glucose monitors (CGMs) plays a critical role in glucose management and the development of artificial pancreas systems. Current CGM performance can be improved by a run-to-run (R2R) strategy based on continuous wear that personalizes sensor calibration parameters using data from previous weeks' use. The proposed strategy determines personalized calibration curve parameter sets (calibration curve slope and intercept, sensitivity drift curve, mean reference error) by tapping into data that is readily available at the end of each week after weekly new sensor reinsertions, minimizing a cost function that penalizes the deviation between expected and actual blood glucose (BG) values. The algorithm was evaluated on 10 in silico subjects within the UVA/Padova metabolic simulator. The enhanced CGM's BG tracking was significantly improved over the standard CGM, decreasing the summed square error by over 20% in the second week and converging to 50% improvement by the 6 th week. The R2R algorithm enhances glucose accuracy week by week based on continuous wear of the device.