  This article presents an identification methodology to capture general
relationships, with application to piecewise nonlinear approximations of model
predictive control for constrained (non)linear systems. The mathematical
formulation takes, at each iteration, the form of a constrained linear (or
quadratic) optimization problem that is mathematically feasible as well as
numerically tractable. The efficiency of the devised methodology is
demonstrated via two industrial applications. Results suggest the possibility
to achieve high approximate precision with limited number of regions, leading
to a significant reduction in computation time when compared to the
state-of-the-art implicit model predictive control solvers.
