Department of Electrical, Electronic and Automatic Engineering, University of Girona, 17004 Girona, Spain.
Automatic Control Department-Campus de Terrassa, Universitat Politècnica de Catalunya (UPC), 08222 Terrassa, Spain.
Sensors (Basel). 2021 Oct 27;21(21):7117. doi: 10.3390/s21217117.
The use of automated insulin delivery systems has become a reality for people with type 1 diabetes (T1D), with several hybrid systems already on the market. One of the particularities of this technology is that the patient is in the loop. People with T1D are the plant to control and also a plant operator, because they may have to provide information to the control loop. The most immediate information provided by patients that affects performance and safety are the announcement of meals and exercise. Therefore, to ensure safety and performance, the human factor impact needs to be addressed by designing fault monitoring strategies. In this paper, a monitoring system is developed to diagnose potential patient modes and faults. The monitoring system is based on the residual generation of a bank of observers. To that aim, a linear parameter varying (LPV) polytopic representation of the system is adopted and a bank of Kalman filters is designed using linear matrix inequalities (LMI). The system uncertainty is propagated using a zonotopic-set representation, which allows determining confidence bounds for each of the observer outputs and residuals. For the detection of modes, a hybrid automaton model is generated and diagnosis is performed by interpreting the events and transitions within the automaton. The developed system is tested in simulation, showing the potential benefits of using the proposed approach for artificial pancreas systems.
自动化胰岛素输送系统已成为 1 型糖尿病 (T1D) 患者的现实选择，已有多种混合系统上市。这项技术的一个特点是患者处于反馈回路中。T1D 患者既是控制者，也是操作人员，因为他们可能需要向控制回路提供信息。患者提供的最直接影响性能和安全性的信息是用餐和运动的通知。因此，为了确保安全和性能，需要通过设计故障监测策略来解决人为因素的影响。本文开发了一种监测系统，用于诊断潜在的患者模式和故障。监测系统基于观测器库的残差生成。为此，采用了系统的线性参数时变 (LPV) 多面体表示，并使用线性矩阵不等式 (LMI) 设计了卡尔曼滤波器库。使用 zonotopic-set 表示法传播系统不确定性，这允许确定每个观测器输出和残差的置信区间。为了检测模式，生成了混合自动机模型，并通过解释自动机中的事件和转换来进行诊断。所开发的系统在仿真中进行了测试，展示了在人工胰腺系统中使用所提出方法的潜在优势。
