Type-1 diabetes is a disease characterized by high blood-glucose level. Using a fully automated closed loop control system improves the quality of life for type1 diabetic patients. In this paper, a scalable closed loop blood glucose regulation system which is tuned to each patient is presented. This control system doesn't need any data entry from the patient. A recurrent neural network (RNN) is used as a nonlinear predictor and a fuzzy logic controller (FLC) is used to determine the insulin dosage which is required to regulate the blood glucose level. The insulin infusion is restricted by calculation of insulin on board (IOB) which avoids overdosing of insulin. The performance of the proposed NMPC is evaluated by applying full day meal regime to each patient. The evaluation includes testing in relation to specific life style condition, i.e. fasting, postprandial, fault meal estimation, and exercise as a metabolic disturbance. Our simulation results indicate that, the use of a RNN along with a FLC can decrease the postprandial glucose concentration. The proposed controller can be used in fasting and can avoid severe hypo or hyper-glycemia during fasting. It can also decrease the postprandial glucose concentration and can dynamically respond to different glycemic challenges.