Chakrabarty Ankush, Healey Elizabeth, Shi Dawei, Zavitsanou Stamatina, Doyle Francis J, Dassau Eyal
Control and Dynamical Systems Group, Mitsubishi Electric Research Laboratories, Cambridge, MA, USA.
Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.
IEEE Trans Control Syst Technol. 2020 Nov;28(6):2600-2607. doi: 10.1109/tcst.2019.2939122. Epub 2019 Sep 18.
While artificial pancreas (AP) systems are expected to improve the quality of life among people with type 1 diabetes mellitus (T1DM), the design of convenient systems that optimize the user experience, especially for those with active lifestyles, such as children and adolescents, still remains an open research question. In this work, we introduce an embeddable design and implementation of model predictive control (MPC) of AP systems for people with T1DM that significantly reduces the weight and on-body footprint of the AP system. The embeddable controller is based on a zone MPC that has been evaluated in multiple clinical studies. The proposed embedded zone MPC features a simpler design of the periodic safe zone in the cost function and the utilization of state-of-the-art alternating minimization algorithms for solving the convex programming problems inherent to MPC with linear models subject to convex constraints. Off-line closed-loop data generated by the FDA-accepted UVA/Padova simulator is used to select an optimization algorithm and corresponding tuning parameters. Through hardware-in-the-loop results on a limited-resource Arduino Zero (Feather M0) platform, we demonstrate the potential of the proposed embedded MPC. In spite of resource limitations, our embedded zone MPC manages to achieve comparable performance of that of the full-version zone MPC implemented in a 64-bit desktop for scenarios with/without meal-disturbance compensations. Metrics for performance comparison included median percent time in the euglycemic ([70, 180] mg/dL range) of 84.3% vs. 83.1% for announced meals, with an equivalence test yielding = 0.0013 and 66.2% vs. 66.0% for unannounced meals with = 0.0028.
虽然人工胰腺（AP）系统有望改善1型糖尿病（T1DM）患者的生活质量，但设计方便的系统以优化用户体验，特别是对于儿童和青少年等生活方式活跃的人群，仍然是一个开放的研究问题。在这项工作中，我们介绍了一种可嵌入的设计和实现，用于T1DM患者的AP系统的模型预测控制（MPC），该设计显著降低了AP系统的重量和体表占用面积。可嵌入控制器基于一种区域MPC，该区域MPC已在多项临床研究中得到评估。所提出的嵌入式区域MPC在成本函数中具有更简单的周期性安全区域设计，并利用了先进的交替最小化算法来解决MPC中固有的凸规划问题，该问题具有受凸约束的线性模型。由FDA认可的UVA/帕多瓦模拟器生成的离线闭环数据用于选择优化算法和相应的调整参数。通过在资源有限的Arduino Zero（Feather M0）平台上进行硬件在环测试，我们展示了所提出的嵌入式MPC的潜力。尽管存在资源限制，但对于有/无进餐干扰补偿的场景，我们的嵌入式区域MPC仍能实现与在64位桌面计算机上实现的完整版区域MPC相当的性能。性能比较指标包括：对于已宣布的进餐，血糖正常（[70, 180]mg/dL范围）的中位时间百分比为84.3%对83.1%，等效性检验得出p = 0.0013；对于未宣布的进餐，血糖正常的中位时间百分比为66.2%对66.0%，p = 0.0028。
