In this study, a closed-loop control scheme is proposed for the glucose-insulin regulatory system in type-1 diabetic mellitus (T1DM) patients. Some innovative hybrid glucose-insulin regulators have combined artificial intelligence such as fuzzy logic and genetic algorithm with well known Palumbo model to regulate the blood glucose (BG) level in T1DM patients. However, most of these approaches have focused on the glucose reference tracking, and the qualitative of this tracking such as chattering reduction of insulin injection has not been well-studied. Higher-order sliding mode (HoSM) controllers have been employed to attenuate the effect of chattering. Owing to the delayed nature and non-linear property of glucose-insulin mechanism as well as various unmeasurable disturbances, even the HoSM methods are partly successful. In this study, data fusion of adaptive neuro-fuzzy inference systems optimised by particle swarm optimisation has been presented. The excellent performance of the proposed hybrid controller, i.e. desired BG-level tracking and chattering reduction in the presence of daily glucose-level disturbances is verified.

中文翻译：

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无颤动混合自适应神经模糊推理系统-基于粒子群优化数据融合的BG级控制。

在本研究中，针对 1 型糖尿病 (T1DM) 患者的葡萄糖-胰岛素调节系统提出了一种闭环控制方案。一些创新的混合葡萄糖-胰岛素调节器将模糊逻辑和遗传算法等人工智能与众所周知的Palumbo模型相结合，以调节T1DM患者的血糖（BG）水平。然而，这些方法中的大多数都集中在葡萄糖参考跟踪上，并且这种跟踪的定性（例如胰岛素注射的颤动减少）尚未得到充分研究。已采用高阶滑模 (HoSM) 控制器来减弱颤振的影响。由于葡萄糖 - 胰岛素机制的延迟性和非线性特性以及各种不可测量的干扰，即使是 HoSM 方法也部分成功。在这项研究中，提出了通过粒子群优化优化的自适应神经模糊推理系统的数据融合。验证了所提出的混合控制器的优异性能，即在存在日常葡萄糖水平干扰的情况下所需的 BG 水平跟踪和颤振减少。