Conditional scenario-based model predictive control

This paper proposes a novel MPC approach called conditional scenario-based model predictive con-trol (CSB-MPC), developed for discrete-time linear systems affected by parametric uncertainties and/or additive disturbances, which are correlated and with bounded support. At each control period, a pri-mary set of equiprobable scenarios is generated and subsequently approximated to a new reduced set of conditional scenarios in which each has its respective probabilities of occurrence. This new set is considered for solving an optimal control problem in whose cost function the predicted states and in-puts are penalised according to the probabilities associated with the uncertainties on which they depend in order to give more importance to predictions that involve realisations with a higher probability of occurrence. The performances of this new approach and those of a standard scenario-based MPC are compared through two numerical examples, and the results show greater probabilities of not transgress-ing the state constraints by the former, even when considering a smaller number of scenarios than the scenario-based MPC.& COPY; 2023 The Author(s). Published by Elsevier Inc. on behalf of The Franklin Institute. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )