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基于胰岛素基础率估计的人工胰腺系统自抗扰控制

史大威 杨肖 蔡德恒 牟治宇 刘蔚 纪立农

史大威, 杨肖, 蔡德恒, 牟治宇, 刘蔚, 纪立农. 基于胰岛素基础率估计的人工胰腺系统自抗扰控制. 自动化学报, 2021, 47(5): 1043−1057 doi: 10.16383/j.aas.c200223
引用本文: 史大威, 杨肖, 蔡德恒, 牟治宇, 刘蔚, 纪立农. 基于胰岛素基础率估计的人工胰腺系统自抗扰控制. 自动化学报, 2021, 47(5): 1043−1057 doi: 10.16383/j.aas.c200223
Shi Da-Wei, Yang Xiao, Cai De-Heng, Mou Zhi-Yu, Liu Wei, Ji Li-Nong. Active disturbance rejection control for artificial pancreas system based on insulin basal rate estimation. Acta Automatica Sinica, 2021, 47(5): 1043−1057 doi: 10.16383/j.aas.c200223
Citation: Shi Da-Wei, Yang Xiao, Cai De-Heng, Mou Zhi-Yu, Liu Wei, Ji Li-Nong. Active disturbance rejection control for artificial pancreas system based on insulin basal rate estimation. Acta Automatica Sinica, 2021, 47(5): 1043−1057 doi: 10.16383/j.aas.c200223

基于胰岛素基础率估计的人工胰腺系统自抗扰控制

doi: 10.16383/j.aas.c200223
基金项目: 国家自然科学基金(61973030), 北京市自然科学基金(4192052)资助
详细信息
    作者简介:

    史大威:北京理工大学自动化学院教授. 主要研究方向为复杂采样控制系统分析与设计及在生物医学、机器人及工业过程中的应用. 本文通信作者. E-mail: daweishi@bit.edu.cn

    杨肖:北京理工大学自动化学院硕士研究生. 主要研究方向为闭环给药系统控制算法设计与实现以及移动应用软件设计. E-mail: yangxiao@bit.edu.cn

    蔡德恒:北京理工大学自动化学院博士研究生. 主要研究方向为事件触发的采样控制、估计与学习以及闭环给药系统控制算法设计与实现. E-mail: dehengcai@bit.edu.cn

    牟治宇:清华大学自动化系硕士研究生. 主要研究方向为移动应用软件设计. E-mail: mouzy20@mails.tsinghua.edu.cn

    刘蔚:北京大学人民医院内分泌科主治医师. 主要研究方向为糖尿病分子病因学. E-mail: liuwei850217@163.com

    纪立农:北京大学人民医院内分泌科主任, 北京大学糖尿病中心共同主任. 主要研究方向为糖尿病分子病因学和转化医学. E-mail: jiln@bjmu.edu.cn

  • 中图分类号: 10.16383/j.aas.c200223

Active Disturbance Rejection Control for Artificial Pancreas System Based on Insulin Basal Rate Estimation

Funds: Supported by National Natural Science Foundation of China (61973030) and Natural Science Foundation of Beijing (4192052)
More Information
    Author Bio:

    SHI Da-Wei Professor at the School of Automation, Beijing Institute of Technology. His research interest covers analysis and synthesis of advanced sampled-data control systems, with applications to biomedical engineering, robotics and motion systems. Corresponding author of this paper

    YANG Xiao Master student at the School of Automation, Beijing Institute of Technology. Her research interest covers closed-loop drug delivery systems and design of mobile application

    CAI De-Heng Ph.D. candidate at the School of Automation, Beijing Institute of Technology. His research interest covers event-triggered sampled-data control, state estimation and machine learning, and closed-loop drug delivery systems

    MOU Zhi-Yu Master student at the Department of Automation, Tsinghua University. His main research interest is mobile application software design

    LIU Wei Physician in the Endocrinology Department of Peking University People' s Hospital. Her research interest covers molecular etiology of diabetes

    JI Li-Nong Director of the Department of Endocrinology, Peking University People' s Hospital, and co-director of Peking University Diabetes Center. His research interest covers molecular etiology of diabetes and translational medicine

  • 摘要:

    胰岛素基础率是人工胰腺系统实现人体血糖闭环控制的基准, 但该变量在临床治疗中难以准确确定. 针对这一问题, 本文设计了一种基于胰岛素基础率动态估计的人工胰腺自抗扰控制方法, 通过扩张状态观测器(Extended state observer, ESO)实时估计血糖代谢过程中的内部与外界干扰, 构建具备参数自适应能力的反馈控制律和胰岛素注射安全约束, 实现血糖闭环调控能力的有效改善. 在此基础上, 本文设计了基于移动设备和蓝牙模块的人工胰腺软件系统, 并通过美国食品药品监督管理局(Food and Drug Administration, FDA)接受的UVA/Padova T1DM仿真平台完成算法的比较仿真与功能测试. 本文的工作将为后续人工胰腺临床试验的开展提供方法基础和技术支持, 也为我国糖尿病患者血糖管理的改善提供精准医学治疗手段.

  • 图  1  控制器结构图

    Fig.  1  Block diagram of the proposed controller

    图  2  血糖代谢过程生理模型

    Fig.  2  Physiological model of glucose metabolism

    图  3  人工胰腺系统仿真平台数据传输示意图

    Fig.  3  Schematic diagram of data transmission in the artificial pancreas simulation platform

    图  4  APP用户图形界面: “主页”、“能量管理”、 “历史”和“我的”

    Fig.  4  User interface of the APP: “Homepage”, “Energy management”, “History” and “User”

    图  5  在血糖平稳、上升、下降及餐前补充大剂量胰岛素状态下, 血糖及胰岛素输注数据的实时传输及显示

    Fig.  5  Real-time transmission and visualization of blood glucose and insulin infusion data for steady, ascending and descending blood glucose traces, and announced meals

    图  6  在胰岛素基础率偏低情况下, 餐前补充大剂量胰岛素时, 本控制算法和zMPC控制算法在血糖调节和胰岛素输注方面的表现

    Fig.  6  Performance comparison of the proposed controller and the zMPC controller for announced meals in terms of glucose regulation and insulin deliver action for the case of under-estimated basal rate

    图  7  在胰岛素基础率偏低情况下, 餐前未补充大剂量胰岛素时, 本控制算法和zMPC控制算法在血糖调节和胰岛素输注方面的表现

    Fig.  7  Performance comparison of the proposed controller and the zMPC controller for unannounced meals in terms of glucose regulation and insulin deliver action for the case of under-estimated basal rate

    图  8  在胰岛素基础率偏高情况下, 餐前补充大剂量胰岛素时, 本控制算法和zMPC控制算法在血糖调节和胰岛素输注方面的表现

    Fig.  8  Performance comparison of the proposed controller and the zMPC controller for announced meals in terms of glucose regulation and insulin deliver action for the case of over-estimated basal rate

    图  9  在胰岛素基础率偏高情况下, 餐前未补充大剂量胰岛素时, 本控制算法和zMPC控制算法在血糖调节和胰岛素输注方面的表现

    Fig.  9  Performance comparison of the proposed controller and the zMPC controller for unannounced meals in terms of glucose regulation and insulin deliver action for the case of over-estimated basal rate

    图  10  三种控制策略下的仿真对比图

    Fig.  10  Comparison diagram of simulations under three control strategies

    图  11  观测值$ z_1 $$ y $的对比图

    Fig.  11  Comparison diagram of observations $ z_1 $ and $ y $

    图  12  观测值$ z_2 $与血糖变化率对比图

    Fig.  12  Comparison diagram of observations $ z_2 $ and glucose change rate

    图  13  系统干扰观测值$ z_3 $示意图

    Fig.  13  System disturbance observations $ z_3 $

    表  1  线性反馈参数设计

    Table  1  Parameter design for linear feedback

    参数 血糖上升阶段 血糖下降阶段
    ${y \le 275\;{\rm{mg/dL} } }$ ${ {y > 275}\;{\rm{mg/dL} } }$
    k1(y)0.0001 × exp(h)0.00001 × exp(−h)0.00015 × exp(h)
    k2(y)0.002 × exp(h)0.001 × exp(h)0.002 × exp(h)
    k310.151
    注: $h = (y - {G_r})/y$
    下载: 导出CSV

    表  2  正常胰岛素基础率下本控制算法与zMPC控制算法的评估结果

    Table  2  Evaluation results of the proposed controller and the zMPC controller at normal insulin basal rate

    血糖控制性能指标 餐前补充大剂量胰岛素 餐前未补充大剂量胰岛素
    zMPC 本算法 zMPC 本算法
    < 54 mg/dL 时间比率 (%) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0)
    < 70 mg/dL 时间比率 (%) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0)
    70 ~ 180 mg/dL 时间比率 (%) 92.4 (10.2) 92.5 (7.8) 72.4 (13.4) 71.8 (9.4)
    > 250 mg/dL 时间比率 (%) 0 (0.0) 0 (0.0) 1.2 (9.5) 2.5 (9.2)
    血糖平均值 (mg/dL) 135.4 (6.1) 133.9 (6.4) 151.7 (19.9) 152.2 (13.9)
    标准差 (mg/dL) 28.2 (8.2) 28.0 (4.9) 44.9 (14.1) 45.8 (14.6)
    7:00 血糖平均值 (mg/dL) 121.0 (15.2) 116.0 (15.5) 121 (15.5) 118.7 (15.0)
    注: 统计数据以中位数 (四分位距) 的形式表示.
    下载: 导出CSV

    表  4  2倍正常胰岛素基础率下本控制算法与zMPC控制算法的评估结果

    Table  4  Evaluation results of the proposed controller and the zMPC controller at 2 times of normal insulin basal rate

    血糖控制性能指标 餐前补充大剂量胰岛素 餐前未补充大剂量胰岛素
    zMPC 本算法 zMPC 本算法
    < 54 mg/dL 时间比率 (%) 7.9 (10.0) 0 (0.0) 7.8 (10.9) 0 (0.0)
    < 70 mg/dL 时间比率 (%) 19.9 (9.3) 0 (0.8) 18.4 (9.2) 0 (0.0)
    70 ~ 180 mg/dL 时间比率 (%) 77.4 (10.2) 93.4 (6.2) 67.8 (12.8) 74.4 (12.6)
    > 250 mg/dL 时间比率 (%) 0 (0.0) 0 (0.0) 0 (3.9) 2.2 (9.0)
    血糖平均值 (mg/dL) 101.2 (6.2) 130.2 (6.8) 113.5 (14.4) 149.5 (11.9)
    标准差 (mg/dL) 35.7 (6.8) 28.3 (5.8) 50.8 (11.0) 45.2 (14.1)
    7:00 血糖平均值 (mg/dL) 83.0 (38.0) 111.0 (26.0) 78.5 (35.0) 113.0 (27.0)
    注: 统计数据以中位数 (四分位距) 的形式表示.
    下载: 导出CSV

    表  3  0.5倍正常胰岛素基础率下本控制算法与zMPC控制算法的评估结果

    Table  3  Evaluation results of the proposed controller and the zMPC controller at 0.5 times of normal insulin basal rate

    血糖控制性能指标 餐前补充大剂量胰岛素 餐前未补充大剂量胰岛素
    zMPC 本算法 zMPC 本算法
    < 54 mg/dL 时间比率 (%) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0)
    < 70 mg/dL 时间比率 (%) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0)
    70 ~ 180 mg/dL 时间比率 (%) 66.6 (9.7) 92.0 (7.4) 38.5 (42.1) 69.6 (9.3)
    > 250 mg/dL 时间比率 (%) 0 (0.0) 0 (0.0) 18.5 (27.9) 5.0 (11.6)
    血糖平均值 (mg/dL) 171.8 (5.9) 135.5 (6.9) 204.9 (49.0) 159.8 (11.7)
    标准差 (mg/dL) 24.1 (5.9) 27.7 (5.5) 43.2 (13.5) 45.8 (15.6)
    7:00 血糖平均值 (mg/dL) 164.0 (26.5) 117.0 (26.0) 167.5 (28.0) 124.0 (27.0)
    注: 统计数据以中位数 (四分位距) 的形式表示.
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-04-16
  • 录用日期:  2020-06-15
  • 网络出版日期:  2021-05-21
  • 刊出日期:  2021-05-20

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