  This paper develops a data-driven learning framework for approximating the
feasible region and invariant set of a nonlinear system under the nonlinear
Model Predictive Control (MPC) scheme. The developed approach is based on the
feasibility information of a point-wise data set using low-discrepancy
sequence. Using kernel-based Support Vector Machine (SVM) learning, we
construct outer and inner approximations of the boundary of the feasible region
and then, obtain the feasible region of MPC for the system. Furthermore, we
extend our approach to the perturbed nonlinear systems using set-theoretic
method. Finally, an illustrative numerical example is provided to show the
effectiveness of the proposed approach.
