IEEE Trans Biomed Eng. 2018 Aug;65(8):1859-1870. doi: 10.1109/TBME.2017.2783238. Epub 2017 Dec 13.
Zone model predictive control has proven to be an effective closed-loop method to regulate blood glucose for people with type 1 diabetes (T1D). In this paper, we present a universal model-free optimization scheme for adapting the zone for T1D patients individually. The adaptation is based on a clinical glycemic risk index named relative regularized glycemic penalty index (rrGPI), which is calculated from glucose measurements by a continuous glucose monitor. The scheme's objective is to minimize rrGPI by simultaneously modulating a controller's blood glucose target zone's upper bound and lower bound. The adaptation mechanism is based on extremum seeking control, in which the zone boundaries are driven by gradient estimation obtained by continuously sinusoidally modulating and demodulating the rrGPI readings. To improve the adaptation method's robustness against uncertainties, a decaying feedback gain and a vanishing dither signal are employed. in-silico trials suggested that the personalized optimized zone can be reached within a week of adaptation. Both for announced and unannounced meals, the proposed method outperforms the fixed zone [80, 140] mg/dL, which has been employed in the authors' clinical trials. It is also shown that the developed method has strong robustness against real-life uncertainties.
区域模型预测控制已被证明是一种有效的闭环方法，可用于调节 1 型糖尿病（T1D）患者的血糖。本文提出了一种通用的无模型优化方案，用于为每位 T1D 患者单独调整区域。这种调整基于一种名为相对正则化血糖惩罚指数（rrGPI）的临床血糖风险指数，该指数是通过连续血糖监测器测量的血糖值计算得出的。该方案的目标是通过同时调节控制器的血糖目标区域的上限和下限来最小化 rrGPI。适应机制基于极值搜索控制，其中通过连续正弦调制和解调 rrGPI 读数来驱动区域边界的梯度估计。为了提高适应方法对不确定性的鲁棒性，采用了衰减反馈增益和消失的抖动信号。仿真试验表明，个性化优化区域可以在一周的适应时间内达到。对于宣布和未宣布的进餐，所提出的方法优于已在作者临床试验中使用的固定区域 [80,140]mg/dL。还表明，所开发的方法对现实生活中的不确定性具有很强的鲁棒性。
