Velocity-weighting & velocity-penalty MPC of an artificial pancreas: Improved safety & performance.

A novel Model Predictive Control (MPC) law for the closed-loop operation of an Artificial Pancreas (AP) to treat type 1 diabetes is proposed. The contribution of this paper is to simultaneously enhance both the safety and performance of an AP, by reducing the incidence of controller-induced hypoglycemia, and by promoting assertive hyperglycemia correction. This is achieved by integrating two MPC features separately introduced by the authors previously to independently improve the control performance with respect to these two coupled issues.

Extremum Seeking Control for Personalized Zone Adaptation in Model Predictive Control for Type 1 Diabetes.

Zone model predictive control has proven to be an effective closed-loop method to regulate blood glucose for people with type 1 diabetes (T1D). In this paper, we present a universal model-free optimization scheme for adapting the zone for T1D patients individually. The adaptation is based on a clinical glycemic risk index named relative regularized glycemic penalty index (rrGPI), which is calculated from glucose measurements by a continuous glucose monitor.

Real-Time Detection of Infusion Site Failures in a Closed-Loop Artificial Pancreas.

Background: As evidence emerges that artificial pancreas systems improve clinical outcomes for patients with type 1 diabetes, the burden of this disease will hopefully begin to be alleviated for many patients and caregivers. However, reliance on automated insulin delivery potentially means patients will be slower to act when devices stop functioning appropriately. One such scenario involves an insulin infusion site failure, where the insulin that is recorded as delivered fails to affect the patient's glucose as expected.

Twelve-Week 24/7 Ambulatory Artificial Pancreas With Weekly Adaptation of Insulin Delivery Settings: Effect on Hemoglobin A and Hypoglycemia.

Objective: Artificial pancreas (AP) systems are best positioned for optimal treatment of type 1 diabetes (T1D) and are currently being tested in outpatient clinical trials. Our consortium developed and tested a novel adaptive AP in an outpatient, single-arm, uncontrolled multicenter clinical trial lasting 12 weeks.

Research Design And Methods: Thirty adults with T1D completed a continuous glucose monitor (CGM)-augmented 1-week sensor-augmented pump (SAP) period.

Application of Zone Model Predictive Control Artificial Pancreas During Extended Use of Infusion Set and Sensor: A Randomized Crossover-Controlled Home-Use Trial.

Objective: As artificial pancreas (AP) becomes standard of care, consideration of extended use of insulin infusion sets (IIS) and continuous glucose monitors (CGMs) becomes vital. We conducted an outpatient randomized crossover study to test the safety and efficacy of a zone model predictive control (zone-MPC)-based AP system versus sensor augmented pump (SAP) therapy in which IIS and CGM failures were provoked via extended wear to 7 and 21 days, respectively.

Research Design And Methods: A smartphone-based AP system was used by 19 adults (median age 23 years [IQR 10], mean 8.

Outpatient Closed-Loop Control with Unannounced Moderate Exercise in Adolescents Using Zone Model Predictive Control.

Background: The artificial pancreas (AP) has the potential to improve glycemic control in adolescents. This article presents the first evaluation in adolescents of the Zone Model Predictive Control and Health Monitoring System (ZMPC+HMS) AP algorithms, and their first evaluation in a supervised outpatient setting with frequent exercise.

Materials And Methods: Adolescents with type 1 diabetes underwent 3 days of closed-loop control (CLC) in a hotel setting with the ZMPC+HMS algorithms on the Diabetes Assistant platform.

Enhanced Model Predictive Control (eMPC) Strategy for Automated Glucose Control.

Development of an effective artificial pancreas (AP) controller to deliver insulin autonomously to people with type 1 diabetes mellitus is a difficult task. In this paper, three enhancements to a clinically validated AP model predictive controller (MPC) are proposed that address major challenges facing automated blood glucose control, and are then evaluated by both tests and clinical trials. First, the core model of insulin-blood glucose dynamics utilized in the MPC is expanded with a medically inspired personalization scheme to improve controller responses in the face of inter- and intra-individual variations in insulin sensitivity.

Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas.

Objective: To evaluate two widely used control algorithms for an artificial pancreas (AP) under nonideal but comparable clinical conditions.

Research Design And Methods: After a pilot safety and feasibility study (n = 10), closed-loop control (CLC) was evaluated in a randomized, crossover trial of 20 additional adults with type 1 diabetes. Personalized model predictive control (MPC) and proportional integral derivative (PID) algorithms were compared in supervised 27.

Reducing Glucose Variability Due to Meals and Postprandial Exercise in T1DM Using Switched LPV Control: In Silico Studies.

Background: Time-varying dynamics is one of the main issues for achieving safe blood glucose control in type 1 diabetes mellitus (T1DM) patients. In addition, the typical disturbances considered for controller design are meals, which increase the glucose level, and physical activity (PA), which increases the subject's sensitivity to insulin. In previous works the authors have applied a linear parameter-varying (LPV) control technique to manage unannounced meals.

Switched LPV Glucose Control in Type 1 Diabetes.

Objective: The purpose of this paper is to regulate the blood glucose level in Type 1 Diabetes Mellitus patients with a practical and flexible procedure that can switch among a finite number of distinct controllers, depending on the user's choice.

Methods: A switched linear parameter-varying controller with multiple switching regions, related to hypo-, hyper-, and euglycemia situations, is designed. The key feature is to arrange the controller into a framework that provides stability and performance guaranty.

Adjustment of Open-Loop Settings to Improve Closed-Loop Results in Type 1 Diabetes: A Multicenter Randomized Trial.

Context: Closed-loop control (CLC) relies on an individual's open-loop insulin pump settings to initialize the system. Optimizing open-loop settings before using CLC usually requires significant time and effort.

Objective: The objective was to investigate the effects of a one-time algorithmic adjustment of basal rate and insulin to carbohydrate ratio open-loop settings on the performance of CLC.

Velocity-weighting to prevent controller-induced hypoglycemia in MPC of an artificial pancreas to treat T1DM.

The design of a Model Predictive Control (MPC) strategy for the closed-loop operation of an Artificial Pancreas (AP) to treat type 1 diabetes mellitus is considered. The contribution of this paper is to propose a velocity-weighting mechanism, within an MPC problem's cost function, that facilitates penalizing predicted hyperglycemic blood-glucose excursions based on the predicted blood-glucose levels' rates of change. The method provides the control designer some freedom for independently shaping the AP's versus responses to hyperglycemic excursions; of particular emphasis in this paper is the downhill response.

Tackling problem nonlinearities & delays via asymmetric, state-dependent objective costs in MPC of an artificial pancreas.

The design of a Model Predictive Control (MPC) law for an Artificial Pancreas (AP) that automatically delivers insulin to people with type 1 diabetes mellitus is considered. An MPC law was recently proposed that exploits the simplicity of linear dynamical models, but is in two ways a 'nonlinear' departure of standard linear MPC, while circumnavigating the complexity of cumbersome, fully nonlinear MPC approaches. The first of two issues focused on is the nonlinearity of the control problem, and it is demonstrated how this can be tackled via asymmetric objective functions.

Design of an Artificial Pancreas using Zone Model Predictive Control with a Moving Horizon State Estimator.

A zone model predictive control (zone-MPC) algorithm that utilizes the Moving Horizon State Estimator (MHSE) is presented. The control application is an artificial pancreas for treating people with type 1 diabetes mellitus. During the meal challenge, the prediction quality of the zone-MPC algorithm with the MHSE was significantly better than when using the current Luenberger observer to provide the state estimate.

Moving-horizon-like state estimation via continuous glucose monitor feedback in MPC of an artificial pancreas for type 1 diabetes.

An extension of a novel state estimation scheme is presented. The proposed method is developed for model predictive control (MPC) of an artificial pancreas for automatic insulin delivery to people with type 1 diabetes mellitus; specifically, glycemia control based on feedback by a continuous glucose monitor. The state estimation strategy is akin to moving-horizon estimation, but effectively exploits knowledge of sensor recalibrations, ameliorates the effects of delays between measurements and the controller call, and accommodates irregularly sampled output measurements.

State Estimation with Sensor Recalibrations and Asynchronous Measurements for MPC of an Artificial Pancreas to Treat T1DM.

A novel state estimation scheme is proposed for use in Model Predictive Control (MPC) of an artificial pancreas based on Continuous Glucose Monitor (CGM) feedback, for treating type 1 diabetes mellitus. The performance of MPC strategies heavily depends on the initial condition of the predictions, typically characterized by a state estimator. Commonly employed Luenberger-observers and Kalman-filters are effective much of the time, but suffer limitations.

Reducing risks in type 1 diabetes using H∞ control.

A control scheme was designed in order to reduce the risks of hyperglycemia and hypoglycemia in type 1 diabetes mellitus (T1DM). This structure is composed of three main components: an H∞ robust controller, an insulin feedback loop (IFL), and a safety mechanism (SM). A control-relevant model that is employed to design the robust controller is identified.

MPC Design for Rapid Pump-Attenuation and Expedited Hyperglycemia Response to Treat T1DM with an Artificial Pancreas.

The design of a Model Predictive Control (MPC) strategy for the closed-loop operation of an Artificial Pancreas (AP) for treating Type 1 Diabetes Mellitus (T1DM) is considered in this paper. The contribution of this paper is to propose two changes to the usual structure of the MPC problems typically considered for control of an AP. The first proposed change is to replace the symmetric, quadratic input cost function with an asymmetric, quadratic function, allowing negative control inputs to be penalized less than positive ones.

Periodic-zone model predictive control for diurnal closed-loop operation of an artificial pancreas.

Background: The objective of this research is an artificial pancreas (AP) that performs automatic regulation of blood glucose levels in people with type 1 diabetes mellitus. This article describes a control strategy that performs algorithmic insulin dosing for maintaining safe blood glucose levels over prolonged, overnight periods of time and furthermore was designed with outpatient, multiday deployment in mind. Of particular concern is the prevention of nocturnal hypoglycemia, because during sleep, subjects cannot monitor themselves and may not respond to alarms.