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Model Predictive Control with Dynamic Positive Input Extension for Artificial Pancreas Applications

文献类型： 会议论文

第一作者： Gy?rgy Eigner

作者： Gy?rgy Eigner 1 ; Kamilla Novák 2 ; Máté Siket 3 ; Levente Kovács 1 ; Dániel András Drexler 1 ; Imre Rudas 2 ;

作者机构： 1.Physiological Controls Research Center, óbuda University|Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Obuda University

2.Physiological Controls Research Center, óbuda University

3.Physiological Controls Research Center, óbuda University|Applied Informatics and Applied Mathematics Doctoral School, Obuda University|Institute for Computer Science and Control, Budapest, Hungary

关键词： Predictive models;Mathematical models;Physiology;Glucose;Pancreas;Predictive control;Tumors

会议名称： IEEE International Conference on Systems, Man, and Cybernetics

主办单位：

页码： 1313-1319

摘要： Like many physiological systems, the various mathematical models describing the glucose-insulin system can only have non-negative inputs. Thus, the control method — often model predictive control in artificial pancreas systems—must provide a non-negative control signal. Existing solutions include saturation and constrained optimization. In this paper, we propose a dynamic extension of the patient model as a way of ensuring the positivity of the control signal, a method previously applied in tumor growth control. We evaluate the controller in a closed-loop simulation and compare the results with a controller using saturation. Our simulations show that control performance with the extended model can reach or exceed the performance achieved with saturation.

分类号： tp13-53

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