A model-based algorithm for blood glucose control in type I diabetic patients

A model-based algorithm for blood glucose control in type I diabetic patients
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DOI:
10.1109/10.740877
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发表时间:
1999-02-01
影响因子:
4.6
通讯作者:
Peppas, NA
Peppas, NA
中科院分区:
工程技术2区
文献类型:
--
作者:
Parker, RS;Doyle, FJ;Peppas, NA

文献摘要

被引文献

相似文献

开发了基于模型的预测控制算法,以使用闭环胰岛素输注泵维持 I 型糖尿病患者的正常血糖。利用房室建模技术,构建了糖尿病患者的基本模型。由此产生的十九阶非线性药代动力学-药效学表示用于控制器合成。通过投影到拉盖尔基上过滤脉冲响应系数,可以从噪声患者数据中线性识别输入输出模型。使用确定的阶跃响应模型开发线性模型预测控制器。检查控制器对未测量的干扰抑制(50 g 口服葡萄糖耐量测试)的性能。通过设计第二个控制器来改进葡萄糖设定点跟踪性能,该控制器替换了更详细的内部模型,包括状态估计和用于输入输出表示的卡尔曼滤波器。在存在测量噪声的情况下,状态估计控制器将葡萄糖维持在设定点的 15 mg/dl 以内。在无噪声条件下,使用状态估计的基于模型的预测控制器优于文献中的内部模型控制器(降低了 49.4%) 下冲和稳定时间减少 45.7%),这些结果证明了胰岛素输注泵中血糖中心预测算法的潜在用途。
A model-based predictive control algorithm is developed to maintain normoglycemia in the Type I diabetic patient using a closed-loop insulin infusion pump, Utilizing compartmental modeling techniques, a fundamental model of the diabetic patient is constructed. The resulting nineteenth-order nonlinear pharmacokinetic-pharmacodynamic representation is used in controller synthesis. Linear identification of an input-output model from noisy patient data is performed by filtering the impulse-response coefficients via projection onto the Laguerre basis. A linear model predictive controller is developed using the identified step response model. Controller performance for unmeasured disturbance rejection (50 g oral glucose tolerance test) is examined. Glucose setpoint tracking performance is improved by designing a second controller which substitutes a more detailed internal model including state-estimation and a Kalman filter for the input-output representation, The state-estimating controller maintains glucose within 15 mg/dl of the setpoint in the presence of measurement noise, Under noise-free conditions, the model based predictive controller using state estimation outperforms an internal model controller from literature (49.4% reduction in undershoot and 45.7% reduction in settling time), These results demonstrate the potential use of predictive algorithms for blood glucose central in an insulin infusion pump.