The complete automated control and delivery of insulin and glucagon in type 1 diabetes is the developing technology for artificial pancreas. This improves the quality of life of a diabetic patient with the precise infusion. The amount of infusion of these hormones is controlled using a control algorithm, which has the prediction property. The control algorithm model predictive control (MPC) predicts one step ahead and infuses the hormones continuously according to the necessity for the regulation of blood glucose. In this research, the authors propose a MPC control algorithm, which is novel for a dual hormone infusion, for a mathematical model such as Sorenson model, and compare it with the insulin alone or single hormone infusion developed with MPC. Since they aim for complete automatic control and regulation, unmeasured disturbances at a random time are integrated and the performance evaluation is projected through statistical analysis. The blood glucose risk index (BGRI) and control variability grid analysis (CVGA) plot gives the additional evaluation for the comparative results of the two controllers claiming 88% performance by dual hormone evaluated through CVGA plot and 2.05 mg/dl average tracking error, 2.20 BGRI. The MPC developed for dual hormone significantly performs better and the time spent in normal glycaemia is longer while eliminating the risk of hyperglycaemia and hypoglycaemia.

中文翻译：

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双激素血糖治疗的设计及使用MPC算法与单激素的比较


1 型糖尿病中胰岛素和胰高血糖素的完全自动化控制和输送是人工胰腺的开发技术。通过精确的输液提高了糖尿病患者的生活质量。这些激素的输注量是使用具有预测特性的控制算法来控制的。控制算法模型预测控制（MPC）提前预测一步，根据血糖调节的需要持续注入激素。在这项研究中，作者针对 Sorenson 模型等数学模型提出了一种对于双激素输注来说新颖的 MPC 控制算法，并将其与使用 MPC 开发的单独胰岛素或单激素输注进行比较。由于它们的目标是完全自动控制和调节，因此会集成随机时间的未测量干扰，并通过统计分析来预测性能评估。血糖风险指数 (BGRI) 和控制变异性网格分析 (CVGA) 图对两个控制器的比较结果进行了额外评估，声称通过 CVGA 图评估的双激素性能为 88%，平均跟踪误差为 2.05 mg/dl，2.20 BGRI。为双激素开发的MPC显着表现更好，处于正常血糖的时间更长，同时消除了高血糖和低血糖的风险。