Fengna Tang1, Youqing Wang1,2. 1. 1 College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China. 2. 2 College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China.
Abstract
BACKGROUND: Blood glucose (BG) regulation is a long-term task for people with diabetes. In recent years, more and more researchers have attempted to achieve automated regulation of BG using automatic control algorithms, called the artificial pancreas (AP) system. In clinical practice, it is equally important to guarantee the treatment effect and reduce the treatment costs. The main motivation of this study is to reduce the cure burden. METHODS: The dynamic R-parameter economic model predictive control (R-EMPC) is chosen to regulate the delivery rates of exogenous hormones (insulin and glucagon). It uses particle swarm optimization (PSO) to optimize the economic cost function and the switching logic between insulin delivery and glucagon delivery is designed based on switching control theory. RESULTS: The proposed method is first tested on the standard subject; the result is compared with the switching PID and the switching MPC. The effect of the dynamic R-parameter on improving the control performance is illustrated by comparing the results of the EMPC and the R-EMPC. Finally, the robustness tests on meal change (size and timing), hormone sensitivity (insulin and glucagon), and subject variability are performed. All results show that the proposed method can improve the control performance and reduce the economic costs. CONCLUSIONS: The simulation results verify the effectiveness of the proposed algorithm on improving the tracking performance, enhancing robustness, and reducing economic costs. The method proposed in this study owns great worth in practical application.
BACKGROUND:Blood glucose (BG) regulation is a long-term task for people with diabetes. In recent years, more and more researchers have attempted to achieve automated regulation of BG using automatic control algorithms, called the artificial pancreas (AP) system. In clinical practice, it is equally important to guarantee the treatment effect and reduce the treatment costs. The main motivation of this study is to reduce the cure burden. METHODS: The dynamic R-parameter economic model predictive control (R-EMPC) is chosen to regulate the delivery rates of exogenous hormones (insulin and glucagon). It uses particle swarm optimization (PSO) to optimize the economic cost function and the switching logic between insulin delivery and glucagon delivery is designed based on switching control theory. RESULTS: The proposed method is first tested on the standard subject; the result is compared with the switching PID and the switching MPC. The effect of the dynamic R-parameter on improving the control performance is illustrated by comparing the results of the EMPC and the R-EMPC. Finally, the robustness tests on meal change (size and timing), hormone sensitivity (insulin and glucagon), and subject variability are performed. All results show that the proposed method can improve the control performance and reduce the economic costs. CONCLUSIONS: The simulation results verify the effectiveness of the proposed algorithm on improving the tracking performance, enhancing robustness, and reducing economic costs. The method proposed in this study owns great worth in practical application.
Entities:
Keywords:
artificial pancreas; dynamic R-parameter; economic cost; economic model predictive control; switching control
Authors: Lalo Magni; Davide M Raimondo; Chiara Dalla Man; Marc Breton; Stephen Patek; Giuseppe De Nicolao; Claudio Cobelli; Boris P Kovatchev Journal: J Diabetes Sci Technol Date: 2008-07
Authors: Malgorzata E Wilinska; Ludovic J Chassin; Helga C Schaller; Lukas Schaupp; Thomas R Pieber; Roman Hovorka Journal: IEEE Trans Biomed Eng Date: 2005-01 Impact factor: 4.538