A Critical Assessment of Algorithms and Challenges in the Development of a Closed-Loop Artificial Pancreas

Abstract

The development of an artificial pancreas is placed in the context of the history of the field of feedback control systems, beginning with the water clock of ancient Greece, and including a discussion of current efforts in the control of complex systems. The first generation of artificial pancreas devices included two manipulated variables (insulin and glucose infusion) and nonlinear functions of the error (difference between desired and measured glucose concentration) to minimize hyperglycemia while avoiding hypoglycemia. Dynamic lags between insulin infusion and glucose measurement were relatively small for these intravenous-based systems. Advances in continuous glucose sensing, fast-acting insulin analogs, and a mature insulin pump market bring us close to commercial realization of a closed-loop artificial pancreas. Model predictive control is discussed in-depth as an approach that is well suited for a closed-loop artificial pancreas. A major challenge that remains is handling an unknown glucose disturbance (meal), and an approach is proposed to base a current insulin infusion action on the predicted effect of a meal on future glucose values. Better "meal models" are needed, as a limited knowledge of the effect of a meal on the future glucose values limits the performance of any control algorithm.

Detection of a Meal Using Continuous Glucose Monitoring

“…Several are based on mathematical models and designed as proportional integral derivative or model predictive control (4 -7); others are based on fuzzy logic (8). However, as was noted by Hovorka et al (9), a number of challenges remain to be solved before the artificial ␤-cell is realized: one of the critical challenges is the regulation of glucose levels after a meal (10). The meal challenge can be met, in principle, by three different approaches.…”

A feedforward–feedback glucose control strategy for type 1 diabetes mellitus

“…A variety of PID control strategies have been developed for diabetes and described in survey papers and articles [7][8][9]11,[37][38][39]. Most of the related evaluations have been based on simulation studies but experimental applications to dogs and humans have also been reported.…”

“…The tuning strategy used in this research was based on minimization of the discrete version of the integral absolute error for a meal challenge: (11) where IAE (mg min/dL) is the integral absolute error, t ∞ is the duration of the simulation, e = G sp − G where G sp is the setpoint (mg/dL), and Δt is the sampling period (5 min). Based on extensive simulations for different meal sizes, the optimal value of τ 4 appeared to be quite constant, and a value of τ 4 = 0.3 min was satisfactory in all cases.…”

“…During the past 30 years, a variety of glucose control strategies based on continuous and intermittent glucose sensing has been reported. Since this literature has been reviewed in recent survey articles [6][7][8][9][10][11][12], we will not present a detailed review here. Most of the relevant literature has been concerned with variations of proportional-integral-derivative (PID) control algorithms that are used in conjunction with an insulin injection (or bolus) at meal time.…”

Hypoglycemia Prevention in Closed-Loop Artificial Pancreas for Patients with Type 1 Diabetes

“…Therefore, hypoglycemia is considered as one of the major limiting factors in achieving tight glycemic control in T1DM (Cryer (2008)). With the inability of conventional therapy to achieve satisfactory glycemic control, and the development in continuous glucose monitoring (CGM) systems and the increasing use of insulin pumps, the idea of developing an artificial pancreas is viewed as the ideal solution for glycemic control in T1DM (Bequette (2005); Hovorka et al (2006); Kumareswaran et al (2009)). The artificial pancreas is an automated closed-loop system that maintains blood glucose levels within the desired range and prevents hypoglycemia, while minimizing or eliminating the need for patient intervention.…”