Abstract: The development of artificial pancreas (AP) technology for deployment in low-energy, embedded devices is contingent upon selecting an efficient control algorithm for regulating glucose in people with type 1 diabetes mellitus (T1DM). The energy consumption of the AP can be lowered by reducing updates of the control model: the number of times the decisionmaking algorithm is invoked to compute an appropriate insulin dose.

Abstract: The development of artificial pancreas (AP) technology for deployment in low-energy, embedded devices is contingent upon selecting an efficient control algorithm for regulating glucose in people with type 1 diabetes mellitus (T1DM). The energy consumption of the AP can be lowered by reducing updates of the control model: the number of times the decisionmaking algorithm is invoked to compute an appropriate insulin dose.

Abstract: The technology described herein relates to control models for artificial pancreas systems, including insulin injections in people with diabetes. The methods provided herein allow for a modular and personalized intervention for the treatment of diabetes using an iterative learning controller (ILC). The ILC allows for long-acting insulin doses to be computationally applied to track a basal glucose concentration reference, a run-to-run (R2R) control policy to update the treatment plan, that progressively meets the recommended glycemic targets.

Abstract: A model-based control scheme consisting of either a proportional-integral-derivative (IMC-PID) controller or a model predictive controller (MPC), with an insulin feedback (IFB) scheme personalized based on a priori subject characteristics and comprising a lower order control-relevant model to obtain PID or MPC controller for artificial pancreas (AP) applications.

Abstract: The present disclosure provides for systems and methods for maintaining glycemic control of a patient. An exemplary method can provide for first receiving glucose data from at least one sensor in an intraperitoneal space of the patient. The method can then provide for processing the received glucose data at a glucose monitoring system to yield processed data. The method can then provide for instructing, by the glucose monitoring system, an insulin infusion pump. Instructing the insulin infusion pump can be based on a closed-loop PID control algorithm and the processed data.

Abstract: A multivariate parameter adaptation approach is disclosed for long-term use of an artificial pancreas using a dual-layer control scheme. The adaptation problem, which can be treated as an optimization problem with an unknown objective function and constraints, may be solved by the proposed BO-assisted multivariate optimization approach. Results showed that the algorithm was able to identify the improperly tuned parameters and smoothly adjust them for improved glucose regulation, despite lifestyle disturbances.

Abstract: A model-based control scheme consisting of either a proportional-integral-derivative (IMC-PID) controller or a model predictive controller (MPC), with an insulin feedback (IFB) scheme personalized based on a priori subject characteristics and comprising a lower order control-relevant model to obtain PID or MPC controller for artificial pancreas (AP) applications.

Abstract: A state-estimator for the estimation or initialization of the state of a discrete-time state-space dynamical model based on sensor measurements of the model output, comprising fitting a continuous-time function to acquired sensor measurement data-points of each model output, and subsequently sampling the continuous time function at exactly the sample-period of the state-space dynamic model for which the state is being estimated or initialized, in order to construct a model state via a synthesized output trajectory.

Abstract: Presented herein are methods, and devices of adaptively tuning a zone based Model Predictive Control (MPC) controller, using at least one processor, which include determining, residuals based on prediction models storing, in a memory, the determined residuals, calculating a trust index by quantifying uncertainty of the prediction models using the stored residuals and tuning the MPC controller, in real time based on the calculated value of the trust index.

Abstract: Methods, devices, algorithms, and systems controlling insulin delivery employ velocity-weighting. Predicted glucose outcomes are penalized with a cost modulated by a factor that is a function of the glucose velocity, wherein glucose outcomes are penalized increasingly less for increasingly negative glucose velocities, when glucose level is high, and/or wherein a hyperglycemic glucose value that is already converging to the euglycemic zone results in less corrective action by the controller than were the hyperglycemic state steady.

Abstract: A model-based control scheme consisting of either a proportional-integral-derivative (IMC-PID) controller or a model predictive controller (MPC), with an insulin feedback (IFB) scheme personalized based on a priori subject characteristics and comprising a lower order control-relevant model to obtain PID or MPC controller for artificial pancreas (AP) applications.

Abstract: A controller for an artificial pancreas for automated insulin delivery to patients with type 1 diabetes mellitus (T1DM) that enforces safe insulin delivery throughout both day and night, wherein the controller employs zone model predictive control, whereby real-time optimization, based on a model of a human's insulin response, is utilized to regulate blood glucose levels to a safe zone, and time-dependent zones that smoothly modulate the controller correction based on the time of day, wherein the controller strategically strives to maintain an 80-140 mg/dL glucose zone during the day, a 110-220 mg/dL zone at night, and a smooth transition of 2 hour duration in between.

Abstract: A model-based control scheme consisting of either a proportional-integral-derivative (IMC-PID) controller or a model predictive controller (MPC), with an insulin feedback (IFB) scheme personalized based on a priori subject characteristics and comprising a lower order control-relevant model to obtain PID or MPC controller for artificial pancreas (AP) applications.

Abstract: A glucose rate increase detector (GRID) for use in an artificial pancreas (AP), wherein the GRID detects in a person persistent increases in glucose associated with a meal, and either triggers a meal bolus to blunt meal peak safely, during closed-loop control, or alerts the person to bolus for a meal, during open-loop control.

Abstract: Presented herein are methods, and devices of adaptively tuning a zone based Model Predictive Control (MPC) controller, using at least one processor, which include determining, residuals based on prediction models storing, in a memory, the determined residuals, calculating a trust index by quantifying uncertainty of the prediction models using the stored residuals and tuning the MPC controller, in real time based on the calculated value of the trust index.

Abstract: The present disclosure provides a hypoglycemia prediction algorithm (HPA) specifically designed for the unique postprandial glycemic patterns characteristic of PBH. This algorithm can predict impending hypoglycemia by performing a series of steps. The steps can include collecting data from at least one sensor. The data can comprise a concentration of glucose in the bloodstream of a subject. The data can be processed using the HPA and impending glucose concentrations can be calculated. The method can then provide for determining whether the predicted glucose concentrations are lower than a hypoglycemic threshold parameter. In response to determining that the predicted glucose concentrations are lower than the hypoglycemic threshold parameter, the method can provide for enacting an impending hypoglycemia protocol.

Abstract: A state-estimator for the estimation or initialization of the state of a discrete-time state-space dynamical model based on sensor measurements of the model output, comprising fitting a continuous-time function to acquired sensor measurement data-points of each model output, and subsequently sampling the continuous time function at exactly the sample-period of the state-space dynamic model for which the state is being estimated or initialized, in order to construct a model state via a synthesized output trajectory.

Abstract: A state-estimator for the estimation or initialization of the state of a discrete-time state-space dynamical model based on sensor measurements of the model output, comprising fitting a continuous-time function to acquired sensor measurement data-points of each model output, and subsequently sampling the continuous time function at exactly the sample-period of the state-space dynamic model for which the state is being estimated or initialized, in order to construct a model state via a synthesized output trajectory.

Abstract: A machine for processing continuous glucose monitoring data and issuing an alert if hypoglycemia is imminent has three modules: (a) a pre-processing module that receives and modulates continuous glucose monitoring data by reducing noise and adjusting for missed data points and shifts due to calibration; (b) a core algorithm module that receives data from the pre-processing module and calculates a rate of change to make a hypoglycemia prediction, and determine if hypoglycemia is imminent; and (c) an alarm mode module that receives data from the core algorithm and issues an audio or visual alert or warning message or a negative feedback signal to an insulin delivery device if hypoglycemia is imminent.

Abstract: The disclosure relates to drug delivery and maintaining multiple defined physiological zones using model predictive control.