Event-Triggered Model Predictive Control for Embedded Artificial Pancreas Systems

Abstract

Our proposed framework integrated seamlessly with a wide variety of popular MPC variants reported in AP research, customizes tradeoff between glycemic regulation and efficacy according to prior design specifications, and eliminates judicious prior selection of controller sampling times.

Adaptive Zone Model Predictive Control of Artificial Pancreas Based on Glucose- and Velocity-Dependent Control Penalties

“…In Laguna Sanz et al [24], a confidence index was incorporated in a zone MPC to allow controller parameter adaptation based on the accuracy of the prediction model in recent history. An MPC with event-triggered controller update strategies was proposed by Chakrabarty et al [25] to reduce energy consumption of an embedded AP while maintaining satisfactory glucose regulation performance. A recent review of adaptive approaches to AP design can be found in Turksoy and Cinar [26].…”

Persuasive Health

“…There are several biological functions which use feedback control and control methodologies are increasingly being used in non-standard applications [57]. These non-standard applications include climate control [29], biological systems [54], economics [47], resource management [35] and medical treatments [7].…”

“…It provides a systematic way of determining inputs in the presence of noise and uncertainty. Techniques from control theory are increasingly being used for interdisciplinary applications such as climate control, economics, and for medical treatments [7,29,47,57]. It has been suggested that methods from control theory could help determine rules for adaptive digital interventions [3, 10-12, 30, 42-44, 55].…”

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

“…Several control algorithms are proposed for AP systems, including proportional-integralderivative control [14][15][16][17][18][19][20][21][22], fuzzy logic control [23][24][25] and model-based predictive controllers such as model predictive control (MPC) [26][27][28][29][30][31][32][33] and generalized predictive control [34][35][36][37][38]. MPC is widely employed in AP systems because of its inherent ability to easily and effectively handle complex systems with control constraints and many input and output variables.…”