论文标题
双激素人造胰腺的非线性模型预测控制和系统识别
Nonlinear Model Predictive Control and System Identification for a Dual-hormone Artificial Pancreas
论文作者
论文摘要
在这项工作中，我们提出了双激素人工胰腺（AP）的开关非线性模型预测控制（NMPC）算法，并使用最大可能性估计（MLE）来识别模型参数。双激素AP由连续的葡萄糖监测器（CGM），对照算法，胰岛素泵和胰高血糖素泵组成。 AP的设计具有启发式，可在胰岛素和胰高血糖素之间切换以及依赖状态的约束。我们将现有的葡萄糖调节模型与胰高血糖素和锻炼进行模拟，并使用更简单的模型进行控制。我们使用在50种1型糖尿病的虚拟人中使用计算机数值模拟中测试AP（NMPC和MLE）。根据模拟模型生成的数据，为每个虚拟人员确定了该系统。模拟显示范围（3.9-10 mmol/L）的平均时间为89.3％，没有降血糖事件。
In this work, we present a switching nonlinear model predictive control (NMPC) algorithm for a dual-hormone artificial pancreas (AP), and we use maximum likelihood estimation (MLE) to identify model parameters. A dual-hormone AP consists of a continuous glucose monitor (CGM), a control algorithm, an insulin pump, and a glucagon pump. The AP is designed with a heuristic to switch between insulin and glucagon as well as state-dependent constraints. We extend an existing glucoregulatory model with glucagon and exercise for simulation, and we use a simpler model for control. We test the AP (NMPC and MLE) using in silico numerical simulations on 50 virtual people with type 1 diabetes. The system is identified for each virtual person based on data generated with the simulation model. The simulations show a mean of 89.3% time in range (3.9-10 mmol/L) and no hypoglycemic events.
