In this work, the adaptive regulation of blood glucose (BG) in type I diabetic (T1D) patients is considered by developing a Multiple Model Adaptive Control (MMAC), where its estimation is based on Magdelaine's long-term glucose-insulin Model. The (MMAC) is built using a bank of Kalman- Bucy Filters (KBFs)with optimal state feedback controllers. Each KBF is based on a particular value of the equilibrium point for which, the optimal Linear Quadratic Servo (LQ-Servo) controller is designed. The total state estimation is resolved by the probabilistic weighted sum of the produced outputs of all filters based on measured glucose signal. Simulation results show that MMAC is capable of providing reliable estimation and regulation of insulin delivery. Moreover, the performance of the controlled glucose/insulin is improved by 99% compared with that when using a single KBF. The MMAC has accurately identified the glucose signal corresponding to the hypothesis models with an average accuracy of 96.4% for 5 tested patients. Robust performance has been tested with different initial conditions and disturbance.

在这项工作中，通过开发多模型自适应控制（MMAC）来考虑对I型糖尿病（T1D）患者的血糖（BG）进行自适应调节，该模型的估计基于Magdelaine的长期葡萄糖-胰岛素模型。（MMAC）使用具有最佳状态反馈控制器的一组Kalman-Bucy滤波器（KBF）构建。每个KBF都基于平衡点的特定值，为此设计了最佳线性二次伺服（LQ-Servo）控制器。基于所测量的葡萄糖信号，通过所有滤波器产生的输出的概率加权总和来解决总状态估计。仿真结果表明，MMAC能够提供可靠的胰岛素输送估计和调节。此外，与使用单一KBF相比，受控葡萄糖/胰岛素的性能提高了99％。MMAC已准确识别出与假设模型相对应的葡萄糖信号，其中5位接受测试的患者的平均准确度为96.4％。已在不同的初始条件和干扰下测试了稳定的性能。