Avoiding constraints redundancy in predictive control optimization routines

This note concentrates on removing redundancy in the set of constraints for the multiparametric quadratic problems (mpQP) related with the constrained predictive control. The feasible domain is treated as a parameterized polyhedron with a focus on its parameterized vertices. The goal is to find a splitting of the parameters (state) space corresponding to domains with regular shape (nonredundant constraints), resulting in a table of regions where the constraints have a minimal representation, so that the online optimization routines can act with better performances. The procedure can be seen as a preprocessor either for the classical QP methods or for the routines based on explicit solutions. For important degrees of redundancy, the proposed technique may bring computational gains for real-time application or on the complexity of the positioning mechanism for evaluating the explicit solution.