Gondhalekar Ravi, Dassau Eyal, Doyle Francis J
Department of Chemical Engineering, University of California Santa Barbara (UCSB), USA.
Proc Am Control Conf. 2014 Jun;2014:4224-4230. doi: 10.1109/ACC.2014.6859247. Epub 2014 Jul 21.
The design of a Model Predictive Control (MPC) strategy for the closed-loop operation of an Artificial Pancreas (AP) for treating Type 1 Diabetes Mellitus (T1DM) is considered in this paper. The contribution of this paper is to propose two changes to the usual structure of the MPC problems typically considered for control of an AP. The first proposed change is to replace the symmetric, quadratic input cost function with an asymmetric, quadratic function, allowing negative control inputs to be penalized less than positive ones. This facilitates rapid pump-suspensions in response to predicted hypoglycemia, while simultaneously permitting the design of a conservative response to hyperglycemia. The second proposed change is to penalize the velocity of the predicted glucose level, where this velocity penalty is based on a cost function that is again asymmetric, but additionally state-dependent. This facilitates the accelerated response to acute, persistent hyperglycemic events, e.g., as induced by unannounced meals. The novel functionality is demonstrated by numerical examples, and the efficacy of the proposed MPC strategy verified using the University of Padova/Virginia metabolic simulator.
本文考虑为治疗1型糖尿病(T1DM)的人工胰腺(AP)闭环操作设计一种模型预测控制(MPC)策略。本文的贡献在于对通常用于AP控制的MPC问题的常规结构提出了两处更改。第一个提出的更改是用非对称二次函数取代对称二次输入成本函数,使得负控制输入的惩罚小于正控制输入。这有助于响应预测的低血糖时快速暂停泵,同时允许设计对高血糖的保守响应。第二个提出的更改是对预测血糖水平的变化率进行惩罚,其中这种变化率惩罚基于一个同样非对称但还依赖于状态的成本函数。这有助于对急性、持续性高血糖事件(例如由未宣布的进食引起的)加快响应。通过数值示例展示了这种新功能,并使用帕多瓦大学/弗吉尼亚代谢模拟器验证了所提出的MPC策略的有效性。