Plasma-insulin-cognizant adaptive model predictive control for artificial pancreas systems

Abstract

An adaptive model predictive control (MPC) algorithm with dynamic adjustments of constraints and objective function weights based on estimates of the plasma insulin concentration (PIC) is proposed for artificial pancreas (AP) systems. A personalized compartment model that translates the infused insulin into estimates of PIC is integrated with a recursive subspace-based system identification to characterize the transient dynamics of glycemic measurements. The system identification approach is able to identify stable, reliable linear time-varying models from closedloop data. An MPC algorithm using the adaptive models is designed to compute the optimal exogenous insulin delivery for AP systems without requiring any manually-entered meal information. A dynamic safety constraint derived from the estimation of PIC is incorporated in the adaptive MPC to improve the efficacy of the AP and prevent insulin overdosing. Simulation case studies demonstrate the performance of the proposed adaptive MPC algorithm.

Computationally Simple Nonlinear MPC Algorithm for Vehicle Obstacle Avoidance With Minimization of Fuel Utilization

“…Using definitions given by Eqs. (12), (13) and 14, the rudimentary MPC optimization problem (2) may be transformed to the following compact matrix-vector representation…”

“…reactors [3] and rectification columns [4]. Recently, MPC algorithms for other systems have been also developed, example applications are: robots [5], quadrotors [6], fuel cells [7], active vibration attenuation systems [8], microgrids [9], fast electric motors [10], blood glucose regulation [11] and artificial pancreas [12].…”

Controlling the AP Controller: Controller Performance Assessment and Modification

“…18,19 Different AP control systems have been designed based on the MPC. 9,11,[20][21][22][23][24][25][26][27][28][29] Due to the complex nature of BGC dynamics in people with T1D, the performance of the MPC techniques developed by using a fixed model or fixed controller parameters for the AP system may not consistently meet the expectations. Model predictive control performance assessment is challenging because degradation in closed-loop performance can arise due to model deficiencies, poor control design parameters, or inappropriate constraints.…”

“…The measurable disturbances estimated from physiological variables are used both in improving glucose predictions and insulin infusion dose decisions, and in providing warnings for consumption of rescue carbs to prevent hypoglycemia. 15,28 The CPAMS is developed to monitor, evaluate, and modify the mAP controller to enhance its performance and safety. 31 In this work, we propose refinements to CPAMS to enhance its performance and improve glucose concentration regulation.…”

“…In our previous work, we have developed an adaptive MPC-based personalized multivariable, multimodule AP system capable of disturbance rejection without manual announcements for meals and exercise. 28,46 To characterize the time-varying glycemic dynamics, a datadriven recursive subspace system identification technique was integrated with a physiological compartmental model to identify adaptive stable models. 28,47 Assessing the performance of the recursively identified models is necessary to ensure that the model is able to provide accurate output predictions for the use in MPC algorithms.…”