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基于多变量识别的MPC用于闭环血糖调节,受个人变化的约束.
Weijie Wang1,2, Shaoping Wang3,4, Yuwei Zhang3
1College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Shanxi, China.
Computer methods in biomechanics and biomedical engineering
|November 20, 2023
概括
这项研究引入了一种新的基于多变量识别的模型预测控制 (mi-MPC) 对于人工胰腺系统. 在1型糖尿病治疗中,mi-MPC有效调节血糖水平,即使没有餐点预告.
科学领域:
- 生物医学工程 生物医学工程
- 控制系统工程 控制系统工程
- 计算生物学 计算生物学
背景情况:
- 人工胰腺系统需要强大的控制器,以便在糖尿病治疗中有效输注胰岛素.
- 葡萄糖代谢的个体间和个体内变化和时间延迟对葡萄糖控制构成重大挑战.
研究的目的:
- 开发基于多变量识别的预测控制模型 (mi-MPC),以克服人工胰腺葡萄糖调节方面的挑战.
- 直接估计和控制血葡萄糖度 (PGC) 以改善糖尿病治疗.
主要方法:
- 建立了一个集成的葡萄糖-胰岛素模型来描述胰岛素吸收,葡萄糖-胰岛素相互作用和葡萄糖运输.
- 一个颗粒过估计器被设计用于识别单个参数和干扰,形成可观测的葡萄糖-胰岛素动态模型.
- 开发了一个mi-MPC控制器,嵌入了已识别的葡萄糖-胰岛素动态模型来直接控制PGC.
主要成果:
- 使用mi-MPC方法,在使用UVa/Padova模拟器的30个in-silico受试者中证明了有效的葡萄糖调节.
- 控制器实现了7.45 mmol/L的平均血葡萄糖度.
- 该系统成功地调节了葡萄糖,而不需要食物预告.
结论:
- 开发的mi-MPC方法整合了已识别的葡萄糖-胰岛素动态模型,为人工胰腺系统提供了一个有前途的方法.
- 这种方法有效地解决了葡萄糖的变化和时间延迟,提高了糖尿病治疗中的葡萄糖控制精度.
- 能够在不需要预告就餐的情况下调节葡萄糖的能力,对于患者的方便和治疗结果来说,这是一个重大进步.
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