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This article presents an identification methodology to capture generalrelationships, with application to piecewise nonlinear approximations of modelpredictive control for constrained (non)linear systems. The mathematicalformulation takes, at each iteration, the form of a constrained linear (orquadratic) optimization problem that is mathematically feasible as well asnumerically tractable. The efficiency of the devised methodology isdemonstrated via two industrial applications. Results suggest the possibilityto achieve high approximate precision with limited number of regions, leadingto a significant reduction in computation time when compared to thestate-of-the-art implicit model predictive control solvers.

Approximate explicit model predictive control via piecewise nonlinear system identification