基于三支决策的双激素人工胰脏模型预测控制算法研究

doi:10.3969/j.issn.1000-1158.2021.05.21

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Abstract At present, there are few studies on the switching mechanism of double-hormone artificial pancreas subsystems, and existing studies need to be improved in terms of operation efficiency and group control quality. To solve the aboved problem, based on the three decision theory and model predictive control algorithm, a model predictive control algorithm of double-hormone artificial pancreas based on three-branch decision theory was proposed. The algorithm used the three decision theory to design the switching rules among the three subsystems. The rule calculated the decision risk value of switching to each subsystem by designing the cost matrix including economic cost and high and low blood sugar risk, so as to ensure the final switching to the subsystem with the minimum risk value. Finally, 33 virtual patients were experimented on a mature UVA simulation platform and the simulation results were analyzed from various aspects. It was proved that the mentioned algorithm could control blood glucose well within the normal range, and had achieved good results in tracking error, risk index, operation efficiency and group control quality of multiple patients.

Key words： metrology double-hormone artificial pancreas switching rule three-branch decision theories model predictive control UVA simulation platform

Received: 24 July 2019 Published: 24 May 2021

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TB99

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	Articles by authors
	SUN Chao
	KONG Xue-hua
	GAO Jun
	LU Kai-xuan
	DU Peng-xiang

Cite this article:

SUN Chao,KONG Xue-hua,GAO Jun, et al. Research on Model Predictive Control Algorithm of Double-hormone Artificial Pancreas Based on Three-branch Decision Theory[J]. Acta Metrologica Sinica, 2021, 42(5): 681-688.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2021.05.21 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2021/V42/I5/681

［1］王友清. 人工胰脏: 现状、挑战与展望［J］. 中国生物医学工程学报, 2013, 32(3): 363-372.
Wang Y Q. Artificial Pancreas: State-of-the-Art, Challenges and Outlook［J］. Chinese Journal of Biomedical Engineering, 2013, 32(3): 363-372.
［2］El-Khatib F H, Jiang J, Damiano E R. A feasibility study of bihormonal closed-loop blood glucose control using dual subcutaneous infusion of insulin and glucagon in ambulatory diabetic swine［J］. J Diabetes Sci Technol, 2009, 3(4): 789-803.
［3］Parker R S, Doyle F J III, Peppas N A. A model-based algorithm for blood glucose control in Type I diabetic patients［J］. IEEE Trans Biomed Eng, 1999, 46(2): 148-157.
［4］Castle J R, Engle J M, Youssef J E L, et al. Novel use of glucagon in a closed-loop system for prevention of hypoglycemia in type 1 diabetes［J］. Diabetes Care, 2010, 33(6): 1282-1287.
［5］Ning H, Wang Y. Bihormonal artificial pancreas system based on switching model predictive control［C］//34th Chinese Control Conference (CCC). Hangzhou, 2015.
［6］Gao X, Wang Y. Closed-loop blood glucose control using dual subcutaneous infusion of insulin and glucagon based on switching PID controller［C］//Proceedings of the 10th World Congress on Intelligent Control and Automation. Beijing, 2012.
［7］Gao X T, Ning H J, Wang Y Q. Systematically in silico comparison of unihormonal and bihormonal artificial pancreas systems［J］. Computational and Mathematical Methods in Medicine, 2013,DOI: 10.1155/2013/712496.
［8］汤凤娜. 双激素人工胰脏的经济模型预测控制［D］. 北京:北京化工大学, 2018.
［9］李冬果, 李林. Bergman最小模型的研究进展［J］. 现代生物医学进展, 2009, 9(4): 764-767+782.
Li D G, Li L. Research Progress of Bergman's Minimal Model［J］. Progress in Modern Biomedicine, 2009, 9(4): 764-767+782.
［10］Gallardo-Hernández A G, González-Olvera M A, Revilla-Monsalve C, et al. Rapid automatic identification of parameters of the Bergman Minimal Model in Sprague-Dawley rats with experimental diabetes for adaptive insulin delivery［J］. Computers in biology and medicine, 2019, 108: 242-248.
［11］Townsend C, Seron M M, Graham G C. Characterisation of Optimal Responses to Pulse Inputs in the Bergman Minimal Model［J］. IFAC-PapersOnLine, 2017, 50(1): 15163-15168.
［12］ Herrero P, Georgiou P, Oliver N, et al. A Composite Model of Glucagon-Glucose Dynamics for In Silico Testing of Bihormonal Glucose Controllers［J］. Journal of Diabetes Science and Technology, 2013, 7(4): 941-951.
［13］Dimitri B, Vladimír B, Morten H, et al. Adaptive model predictive control for a dual-hormone artificial pancreas［J］. Journal of Process Control, 2018, 68: 105-117.
［14］于洪, 王国胤, 姚一豫. 决策粗糙集理论研究现状与展望［J］. 计算机学报, 2015, 38(8): 1628-1639.
Yu H, Wang G Y, Yao Y Y. Current Research and Future Perspectives on Decision-Theoretic Rough Sets［J］. Chinese Journal of Computers, 2015, 38(8): 1628-1639.
［15］毕立恒,朱彦齐. 基于分群粒子群算法的平面度误差评定研究［J］. 计量学报, 2019, 40(6): 980-985.
Bi L H, Zhu Y Q. Flatness Error Evaluation Based on Grouped Particle Swarm Optimization Algorithm［J］. Acta Metrologica Sinica, 2019, 40(6): 980-985.
［16］赵志彪,李瑞,刘彬,等. 基于速度交流的共生多种群粒子群算法［J］. 计量学报, 2020, 41(8): 1012-1022.
Zhao Z B, Li R, Liu B, et al. Symbiosis Multi-population Particle Swarm Optimization Algorithm Based on Velocity Communication［J］. Acta Metrologica Sinica, 2020, 41(8): 1012-1022.
［17］Chiara D M, Micheletto F, Lv D Y, et al. The UVA/PADOVA Type 1 Diabetes Simulator: New Features［J］. Journal of Diabetes Science and Technology, 2014, 8(1): 26-34.