双激素人工胰腺的非线性模型控制与系统辨识
Nonlinear Model Predictive Control and System Identification for a Dual-hormone Artificial Pancreas
摘要：
针对双激素人工胰腺系统，提出了一种切换非线性模型预测控制（NMPC）算法，并采用极大似然估计（MLE）辨识模型参数。双激素AP由连续葡萄糖监测仪（CGM）、控制算法、胰岛素泵和胰高血糖素泵组成。AP被设计为具有在胰岛素和胰高血糖素之间切换的启发式以及状态依赖性约束。我们扩展了现有的glucoregulatory模型与胰高血糖素和运动的模拟，我们使用一个更简单的模型进行控制。我们测试的AP（NMPC和MLE）使用硅数值模拟50虚拟人与1型糖尿病。基于利用仿真模型生成的数据，为每个虚拟人识别系统。模拟显示平均89.3%的时间在范围内（3.9-10 mmol/L），且无低血糖事件。
英文摘要：
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.
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