Simultaneous model predictive control and moving horizon estimation for blood glucose regulation in Type 1 diabetes

Abstract

Summary A new estimation and control approach for the feedback control of an artificial pancreas to treat Type 1 diabetes mellitus is proposed. In particular, we present a new output‐feedback predictive control approach that simultaneously solves the state estimation and control objectives by means of a single min‐max optimization problem. This involves optimizing a cost function with both finite forward and backward horizons with respect to the unknown initial state, unmeasured disturbances and noise, and future control inputs and is similar to simultaneously solving a model predictive control (MPC) problem and a moving horizon estimation (MHE) problem. We incorporate a novel asymmetric output cost to penalize dangerous low blood glucose values more severely than less harmful high blood glucose values. We compare this combined MPC/MHE approach to a control strategy that uses state‐feedback MPC preceded by a Luenberger observer for state estimation. In‐silico results showcase several advantages of this new simultaneous MPC/MHE approach, including fewer hypoglycemic events without increasing the number of hyperglycemic events, faster insulin delivery in response to a meal consumption, and shorter insulin pump suspensions, resulting in smoother blood glucose trajectories.

Proactive Energy Optimization in Residential Buildings with Weather and Market Forecasts

“…Frison et al investigate MHE computation time and conclude MHE can be computed quickly enough to enable real-time control. Consequentially, many recent studies have combined MHE with MPC [58][59][60][61].…”

Stable impulsive zone model predictive control for type 1 diabetic patients based on a long‐term model

“…In the past decade, MPC received an increasing attention as an advanced control strategy to be implemented in an AP device 3‐9 . In general, these formulations use discrete‐time control actions and are based on a simplification of the model of T1DM patient presented in Reference 10 and its linearizations 8,11‐14 . The most popular MPC formulation in AP is the zone MPC 7,14 which, by means of a modified objective cost, considers an entire range of the controlled variable as the target.…”

“…In general, these formulations use discrete‐time control actions and are based on a simplification of the model of T1DM patient presented in Reference 10 and its linearizations 8,11‐14 . The most popular MPC formulation in AP is the zone MPC 7,14 which, by means of a modified objective cost, considers an entire range of the controlled variable as the target. Recent improvements are the consideration of asymmetric cost functions and explicit velocity‐penalties in the MPC formulation 8 …”

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

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