Modeling, Identification, and Control towards Adaptive Personalized Glucose Management for Insulin-Deficient Diabetes

针对胰岛素缺乏型糖尿病的自适应个性化血糖管理的建模、识别和控制

• 批准号: 1200838
• 负责人: Qian Wang
• 金额: $ 30万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1200838&HistoricalAwards=false
• 关键词: Modeling; Identification; Control; towards; Adaptive

The research objective of this award is to develop modeling and control algorithms for personalized adaptive insulin therapy. The algorithms can be used in the artificial pancreas for type-1 diabetes, but also would be useful for glucose management for insulin-dependent type-2 diabetes. This research will characterize patient-specific glucose dynamics by data-driven models with time-varying parameters that can be adjusted for the effects of food intake, physical activity and emotional tone under natural living conditions. This research will also develop adaptive control algorithms that enable a better control of hyperglycemia and prevention of hypoglycemia. Robustness issues will be addressed with respect to uncertainties in estimation of carb content, uncertain time-delay of meal intake, and variations in insulin and carb sensitivities. Deliverables include easy-to-implement real-time parameter estimation algorithms for insulin-glucose kinetics, adaptive control algorithms for insulin delivery, evaluation and validation via a FDA-approved simulator for type-1 diabetes, documentation of research results, and engineering student education.If successful, the results of this research will facilitate the development of a mechanical artificial pancreas by means of providing a control algorithm used to close the loop between glucose sensing and insulin delivery in the artificial pancreas. Patients with type-1 or type-2 diabetes will benefit from the results of this research by lowering the risk of many of the devastating long-term complications. After testing and evaluation through FDA-approved simulation platforms, the results are transformable to be used in insulin pumps for clinical tests. Engineering students will get involved in this research through senior design projects as well as projects in undergraduate- and graduate-level control courses, e.g., design of user-friendly tools in aiding the control of diet and improvement of control algorithms.

该奖项的研究目标是开发个性化自适应胰岛素治疗的建模和控制算法。该算法可用于1型糖尿病的人工胰腺，但也可用于胰岛素依赖型2型糖尿病的葡萄糖管理。这项研究将通过具有时变参数的数据驱动模型来表征患者特定的葡萄糖动力学，这些参数可以根据自然生活条件下的食物摄入量，体力活动和情绪基调的影响进行调整。 这项研究还将开发自适应控制算法，使更好地控制高血糖和预防低血糖。 稳健性问题将解决碳水化合物含量估计的不确定性，不确定的膳食摄入时间延迟，胰岛素和碳水化合物的敏感性变化。可实现的功能包括易于实现的胰岛素-葡萄糖动力学实时参数估计算法、胰岛素输送的自适应控制算法、通过FDA批准的1型糖尿病模拟器进行评估和验证、研究结果文档以及工程学生教育。如果成功，本研究的结果将有助于机械人工胰腺的发展，人工胰腺中葡萄糖传感和胰岛素输送之间的回路。 1型或2型糖尿病患者将受益于这项研究的结果，降低许多破坏性的长期并发症的风险。通过FDA批准的模拟平台进行测试和评估后，结果可转换为用于胰岛素泵的临床测试。 工程专业的学生将通过高级设计项目以及本科和研究生水平的控制课程项目参与这项研究，例如，设计用户友好的工具，帮助控制饮食和改进控制算法。