Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
Math Biosci. 2018 Nov;305:122-132. doi: 10.1016/j.mbs.2018.09.006. Epub 2018 Sep 7.
Currently, artificial pancreas is an alternative treatment instead of insulin therapy for patients with type 1 diabetes mellitus. Closed-loop control of blood glucose level (BGL) is one of the difficult tasks in biomedical engineering field due to nonlinear time-varying dynamics of insulin-glucose relation that is combined with time delays and model uncertainties. In this paper, we propose a novel adaptive fuzzy integral sliding mode control scheme for BGL regulation. System dynamics is identified online using fuzzy logic systems. The presented method is evaluated in silico studies by nine different virtual patients in three different groups for two continuous days. Simulation results demonstrate effective performance of the proposed control scheme of BGL regulation in presence of simultaneous meal and physical exercise disturbances. Comparison of the proposed control method with proportional-integral-derivative (PID) control and model predictive control (MPC) shows a superiority of the adaptive fuzzy integral sliding mode control with regard to two conventional methods of BGL regulation (PID and MPC) and sliding mode control.
目前，人工胰腺是 1 型糖尿病患者替代胰岛素治疗的一种选择。由于胰岛素-血糖关系的非线性时变动态特性与时间延迟和模型不确定性相结合，血糖水平（BGL）的闭环控制是生物医学工程领域的一项难题。在本文中，我们提出了一种新的用于 BGL 调节的自适应模糊积分滑模控制方案。系统动态特性通过模糊逻辑系统在线识别。该方法通过在 3 组 9 个不同虚拟患者中进行 2 天的模拟研究进行了评估。仿真结果表明，在同时存在进餐和体育锻炼干扰的情况下，该控制方案对 BGL 调节具有有效的性能。与比例-积分-微分（PID）控制和模型预测控制（MPC）的比较表明，自适应模糊积分滑模控制在 BGL 调节的两种传统方法（PID 和 MPC）和滑模控制方面具有优越性。
