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

Abstract

Background: Model predictive control (MPC) performance depends on the accuracy of the prediction model implemented by the controller. Complex physiology and modeling limitations often prevent the ability to provide long and accurate glucose predictions, which results in the need to account for prediction errors. Method:Optimal insulin dosage by Zone-MPC is calculated by solving an optimization problem in which a scalar index is minimized by penalizing relative input deviations and glucose predictions out of the reference zone. The controller's tuning parameters are the penalties on the input variable (insulin). Positive and negative relative inputs are penalized differently. A dynamic adaptation of the tuning parameters based on the accuracy of the model in recent history is implemented in this article and compared in silico to aggressive and conservative tunings of the same controller structure.Results: Similar average glucose and time in the safe glucose range (70-180 mg/dL) are achieved for the adaptive design and traditional controller configurations. However, percentage time under 70 mg/dL is significantly reduced, both for announced meals using bolus compensation and unannounced meals with a meal detection algorithm triggered bolus. No differences in the average insulin delivered were observed between the adaptive design and the conservative or aggressive tuning for the bolus strategy, and the adaptive controller delivered less insulin in the other scenario considered. Conclusions:The adaptive strategy provides safe and effective glucose management as well as significant reduction of hypoglycemia events. No abnormal insulin delivery profiles were observed upon the application of the adaptive strategy.

Zone-MPC Automated Insulin Delivery Algorithm Tuned for Pregnancy Complicated by Type 1 Diabetes

“…The baseline zone-MPC design, aimed to succeed in non-pregnancy glucose target range of 70 – 180 mg/dL, showed a performance above 95% time in this range for all but extreme case scenarios explored in this study. The high percentage of time in the non-pregnancy target range obtained by the baseline zone-MPC is consistent with some of the previous in-silico studies on zone-MPC ( 40 , 41 ) Both studies reported above 90% time in 70 - 180 mg/dL while the meals in their scenarios had higher carbohydrate content (i.e., ranging from 50 to 100 grams per meal) compared to our experiments (i.e., 40 grams per meal). In our work, low carbohydrate content of the meals, all meals being announced at or before their intake and accurately, the effect of titrating the insulin treatment parameters to achieve pregnancy-specific glucose control requirements in Scenario A and partially in Scenario C contributed to the high performance of all three controllers.…”

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

“…Spurred by these successes more extensive, long-term trials are planned. In work parallel to that presented in this paper the authors are investigating more extensive personalization (Pinsker et al 2016, Lee, Dassau, Gondhalekar, Seborg, Pinsker & Doyle III 2016), control-oriented models more elaborate than LTI (Colmegna, Sánchez-Peña, Gondhalekar, Dassau & Doyle III 2016 a , b , Colmegna, Sánchez-Peña & Gondhalekar 2016), enhanced cost functions (Lee, Gondhalekar, Dassau & Doyle III 2016, Rebello et al 2017), and real-time adaptation of the control strategy (Laguna Sanz et al 2016, Cao et al 2017). Planned future work focuses on the design of control laws for people that exercise, for a wider range of users, e.g., young children and people with disorders in addition to T1DM, and exploiting non-BG feedback by, e.g., activity trackers.…”

A hybrid dynamic wavelet-based modeling method for blood glucose concentration prediction in type 1 diabetes

“…[ 11 12 ] However, BGC control methods, particularly MPC, require the prediction of BGC. [ 13 14 ] Thus, it is essential to develop a model that can predict BGC. [ 15 ]…”