Parker R S, Doyle F J, Peppas N A
Department of Chemical Engineering, University of Delaware, Newark 19716, USA.
IEEE Trans Biomed Eng. 1999 Feb;46(2):148-57. doi: 10.1109/10.740877.
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 control in an insulin infusion pump.
开发了一种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型糖尿病患者的血糖正常。利用房室建模技术，构建了糖尿病患者的基础模型。所得的十九阶非线性药代动力学-药效学表示用于控制器合成。通过将脉冲响应系数投影到拉盖尔基上进行滤波，从有噪声的患者数据中对输入-输出模型进行线性识别。使用识别出的阶跃响应模型开发了线性模型预测控制器。检查了未测量干扰抑制（50克口服葡萄糖耐量试验）的控制器性能。通过设计第二个控制器来替代输入-输出表示，该控制器使用包括状态估计和卡尔曼滤波器的更详细内部模型，从而提高了葡萄糖设定点跟踪性能。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15mg/dl范围内。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（下冲减少49.4%，建立时间减少45.7%）。这些结果证明了预测算法在胰岛素输注泵中用于血糖控制的潜在用途。
