The paper presents a method for enlarging the terminal region of quasi-infinity horizon nonlinear model predictive control (NMPC) for nonlinear systems with constraints. The main technique builds on the fact that terminal controllers are fictitious and never applied to the system in the quasi-infinite horizon NMPC [1]. Based on T-S fuzzy models of nonlinear systems, we show that a parameter-dependent state feedback law exists such that the corresponding value function and its level set can be served as terminal cost and terminal region. The problem of maximizing the terminal region is formulated as a convex optimization problem based on linear matrix inequalities (LMIs). A numerical example is given to illustrate the effectiveness of the proposed method.