A multi-parametric model predictive control (mpMPC) algorithm for subcutaneous insulin delivery for individuals with type 1 diabetes mellitus (T1DM) that is computationally efficient, robust to variations in insulin sensitivity, and involves minimal burden for the user is proposed. System identification was achieved through impulse response tests feasible for ambulatory conditions on the UVa/Padova simulator adult subjects with T1DM. An alternative means of system identification using readily available clinical parameters was also investigated. A safety constraint was included explicitly in the algorithm formulation using clinical parameters typical of those available to an attending physician. Closed-loop simulations were carried out with daily consumption of 200 g carbohydrate. Controller robustness was assessed by subject/model mismatch scenarios addressing daily, simultaneous variation in insulin sensitivity and meal size with the addition of Gaussian white noise with a standard deviation of 10%. A second-order-plus-time-delay transfer function model fit the validation data with a mean (coefficient of variation) root-mean-square-error (RMSE) of 26 mg/dL (19%) for a 3 h prediction horizon. The resulting control law maintained a low risk Low Blood Glucose Index without any information about carbohydrate consumption for 90% of the subjects. Low-order linear models with clinically meaningful parameters thus provided sufficient information for a model predictive control algorithm to control glycemia. The use of clinical knowledge as a safety constraint can reduce hypoglycemic events, and this same knowledge can further improve glycemic control when used explicitly as the controller model. The resulting mpMPC algorithm was sufficiently compact to be implemented on a simple electronic device.

中文翻译：

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使用临床参数开发用于 1 型糖尿病胰岛素输送的多参数模型预测控制算法

提出了一种用于 1 型糖尿病 (T1DM) 患者皮下胰岛素输送的多参数模型预测控制 (mpMPC) 算法，该算法具有计算效率、对胰岛素敏感性变化的鲁棒性，并且对用户的负担最小。系统识别是通过脉冲响应测试实现的，该测试适用于 UVa/Padova 模拟器成人 T1DM 受试者的动态条件。还研究了使用现成的临床参数进行系统识别的替代方法。使用主治医师可用的典型临床参数在算法公式中明确包含安全约束。每天消耗 200 克碳水化合物进行闭环模拟。控制器稳健性通过主题/模型不匹配场景评估，解决了胰岛素敏感性和膳食量的日常同时变化，并添加了标准偏差为 10% 的高斯白噪声。二阶加时延传递函数模型以 26 mg/dL (19%) 的平均值（变异系数）均方根误差 (RMSE) 拟合验证数据，预测范围为 3 小时. 由此产生的控制法维持了低风险的低血糖指数，没有任何关于 90% 受试者碳水化合物消耗的信息。因此，具有临床意义参数的低阶线性模型为控制血糖的模型预测控制算法提供了足够的信息。使用临床知识作为安全约束可以减少低血糖事件，当明确用作控制器模型时，同样的知识可以进一步改善血糖控制。由此产生的 mpMPC 算法足够紧凑，可以在简单的电子设备上实现。