Department of Instrumentation and Control, Manipal Academy of Higher Education, Manipal Institute of Technology, Manipal, India.
Department of Medicine, Manipal Academy of Higher Education, Kasturba Medical College, Manipal, India.
IET Syst Biol. 2020 Jun;14(3):133-146. doi: 10.1049/iet-syb.2019.0101.
This study elaborates on the design of artificial pancreas using model predictive control algorithm for a comprehensive physiological model such as the Sorensen model, which regulates the blood glucose and can have a longer control time in normal glycaemic region. The main objective of the proposed algorithm is to eliminate the risk of hyper and hypoglycaemia and have a precise infusion of hormones: insulin and glucagon. A single model predictive controller is developed to control the bihormones, insulin, and glucagon for such a development unmeasured disturbance is considered for a random time. The simulation result for the proposed algorithm performed good regulation lowering the hypoglycaemia risk and maintaining the glucose level within the normal glycaemic range. To validate the performance of the tracking of output and setpoint, average tracking error is used and 4.4 mg/dl results are obtained while compared with standard value (14.3 mg/dl).
本研究详细阐述了使用模型预测控制算法设计人工胰腺，该算法针对 Sorensen 等综合生理模型进行了优化，可以在正常血糖区域实现更长时间的控制。该算法的主要目标是消除高血糖和低血糖的风险，并精确输注激素：胰岛素和胰高血糖素。我们开发了一种单一的模型预测控制器来控制这两种激素，胰岛素和胰高血糖素，同时考虑到未测量的干扰会在随机时间发生。所提出的算法的仿真结果表现良好，可以降低低血糖风险，并使血糖水平维持在正常血糖范围内。为了验证输出和设定值跟踪的性能，我们使用了平均跟踪误差，结果为 4.4mg/dl，与标准值（14.3mg/dl）相比。
