基于胰岛素基础率估计的人工胰腺系统自抗扰控制

胰岛素基础率是人工胰腺系统实现人体血糖闭环控制的基准, 但该变量在临床治疗中难以准确确定. 针对这一问题, 本文设计了一种基于胰岛素基础率动态估计的人工胰腺自抗扰控制方法, 通过扩张状态观测器(Extended state observer, ESO)实时估计血糖代谢过程中的内部与外界干扰, 构建具备参数自适应能力的反馈控制律和胰岛素注射安全约束, 实现血糖闭环调控能力的有效改善. 在此基础上, 本文设计了基于移动设备和蓝牙模块的人工胰腺软件系统, 并通过美国食品药品监督管理局(Food and Drug Administration, FDA)接受的UVA/Padova T1DM仿真平台完成算法的比较仿真与功能测试. 本文的工作将为后续人工胰腺临床试验的开展提供方法基础和技术支持, 也为我国糖尿病患者血糖管理的改善提供精准医学治疗手段.

Active Disturbance Rejection Control for Artificial Pancreas System Based on Insulin Basal Rate Estimation

Insulin basal rate provides the reference for closed-loop blood glucose regulation using artificial pancreas systems, but this quantity is usually difficult to determine accurately in clinical practice. In this regard, this paper introduces an active disturbance rejection control method for artificial pancreas systems based on dynamic estimation of the basal rate. To enable improved glucose regulation, an extended state observer (ESO) is employed to estimate the internal and external disturbances in the glucose metabolic process, and a feedback control law and insulin infusion safety constraints that both incorporate parameter adaptation are proposed. Based on the proposed method, an artificial pancreas software system is designed for mobile devices with Bluetooth modules. The proposed results are evaluated through comparative simulations and functionality tests by using the US FDA (Food and Drug Administration)-accepted UVA/Padova T1DM simulator. The obtained results provide methodological and technical support for further clinical studies of artificial pancreas systems, and introduce a precision medicine solution to enhanced glucose management for Chinese patients with diabetes mellitus.