Patient specific artificial pancreas design has been receiving increasing attention lately. In this article, using the chaotic bat algorithm (CBA), Hovorka-Wilinska (H-W) model parameters are estimated from nominal H-W virtual patient data. Using this identified H-W model for the virtual patient, multiple empirical second order plus delay time (SOPDT) models representing glucose-insulin dynamics are derived for the range of blood glucose concentrations (BGCs) considered. Clustering of these models using the k-means algorithm yields three distinct clusters. Implicity enumerated multiparametric model predictive controllers (mpMPCs) are designed using the cluster representatives. A fuzzy logic aggregatioin (FLA) of prediction and control improves the design parsimony. An insulin on board (IOB) safety trigger is designed using FLA of multiple full order linearized CBA-estimated H-W models. The FLA-based mpMPC along with IOB and metal estimation are implemented on an embedded platform and by hardware-in-the loop (HIL) simulation. In silico trials of the regulation of multiple meal disturbances are perfomed on the nominal H-W patient in MATLAB through serial communication with meal estimation errors and varying insulin sensitivity, a very good low blood glucose index (LBGI) of < 1 is observed Control variability grid analysis (CVGA) also supports efficient elimination of hypoglycemic exposure by the designed artificial pancreas.