Glucose level regulation for diabetes mellitus type 1 patients using FPGA neural inverse optimal control

In this paper, the field programmable gate array (FPGA) implementation of a discrete-time inverse neural optimal control for trajectory tracking is proposed to regulate glucose level for type 1 diabetes mellitus (T1DM) patients. For this controller, a control Lyapunov function (CLF) is proposed to obtain an inverse optimal control law in order to calculate the insulin delivery rate, which prevents hyperglycemia and hypoglycemia levels in T1DM patients. Besides this control law minimizes a cost functional. The neural model is obtained from an on-line neural identifier, which uses a recurrent high-order neural network (RHONN), trained with an extended Kalman filter (EKF). A virtual patient is implemented on a PC host computer, which is interconnected with the FPGA controller. This controller constitutes a step forward to develop an autonomous artificial pancreas.