Control-Relevant Models for Glucose Control Using A Priori Patient Characteristics

Abstract

One of the difficulties in the development of a reliable artificial pancreas for people with type 1 diabetes mellitus (T1DM) is the lack of accurate models of an individual's response to insulin. Most control algorithms proposed to control the glucose level in subjects with T1DM are model-based. Avoiding postprandial hypoglycemia ( 60 mg/dl) while minimizing prandial hyperglycemia ( > 180 mg/dl) has shown to be difficult in a closed-loop setting due to the patient-model mismatch. In this paper, control-relevant models are developed for T1DM, as opposed to models that minimize a prediction error. The parameters of these models are chosen conservatively to minimize the likelihood of hypoglycemia events. To limit the conservatism due to large intersubject variability, the models are personalized using a priori patient characteristics. The models are implemented in a zone model predictive control algorithm. The robustness of these controllers is evaluated in silico, where hypoglycemia is completely avoided even after large meal disturbances. The proposed control approach is simple and the controller can be set up by a physician without the need for control expertise.

An Enhanced Model Predictive Control for the Artificial Pancreas Using a Confidence Index Based on Residual Analysis of Past Predictions

“…This control scheme was later expanded using a control oriented prediction strategy proposed by van Heusden et al 31 The cost function for the MPC problem is given by:…”

Periodic-Zone Model Predictive Control for Diurnal Closed-Loop Operation of an Artificial Pancreas

“…The PZMPC strategy was compared with two alternative control strategies. The first alternative was the zone-MPC strategy described previously, 7,17 employing the same blood glucose target zone (equal to the PZMPC daytime zone) at all times of day. This strategy is referred to here as invariant-zone-MPC.…”

“…The primary contribution of this article is the development of a zone-MPC law that strategically reduces the risk of hypoglycemia during the night-assumed to be the time of sleep. The novelty over the time-invariant zone-MPC strategy of Grosman and coauthors 7 and van Heusden and coauthors 17 is that the proposal is periodically time dependent with respect to the time of day. Specifically, during the night, the blood glucose target zone is raised and the bound on the maximum insulin infusion rate is reduced from the values employed during the day.…”

Model-Based Closed-Loop Glucose Control in Type 1 Diabetes: The DiaCon Experience

“…Our controller is an MPC algorithm that addresses these challenges by handling the stochastic CGM and patient variations using a Kalman filter, by controlling the glucose level to a range rather than a specific glucose value, by providing steady state offset free glucose control, by being robust against patient-model mismatch by its definition of target trajectories and use of low-order models, by being individualized to each patient by use of already available parameters, and by including medical expert rules for safety purposes. The controller includes, combines, and extends key principles from model-based proportional-integral-derivative controllers, 23 previous MPC algorithms, [24][25][26][27][28] fuzzy logic controllers, 29 and novel theoretical MPC insights. 30,31 Intravenous glucose was administered on three CL study nights (Figure 2).…”