Neural Inverse Optimal Control via Passivity for Subcutaneous Blood Glucose Regulation in Type 1 Diabetes Mellitus Patients

Abstract

This paper deals with subcutaneous blood glucose level control. Inverse optimal trajectory tracking for discrete time non-linear positive systems is applied. The scheme is developed for MIMO (multi-input, multi-output) affine systems. The control law calculates the subcutaneous insulin delivery rate in order to prevent hyperglycemia and hypoglycemia events. A neural model is obtained from an on-line neural identifier, which uses a recurrent neural network, trained with the extended Kalman filter (EKF); this neural model has an affine form, which permits the applicability of inverse optimal control scheme. The proposed algorithm is tuned to follow a desired trajectory; this trajectory reproduces the glucose absorption of a healthy person. Then this model is used to synthesize an inverse optimal controller in order to regulate the subcutaneous blood glucose level for a Type 1 Diabetes Mellitus patient the applicability of the proposed scheme is illustrated via simulation using a recurrent neural network in order to model the insulin-glucose dynamics. � 2014 TSI� Press.