Conditional scenario-based model predictive control

[EN] This paper proposes a novel MPC approach called conditional scenario-based model predictive control (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 primary 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 inputs 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 transgressing the state constraints by the former, even when considering a smaller number of scenarios than the scenario-based MPC. ; This work was supported in part by the MCIN/AEI/10.13039/501100011033 under Grant PID2020-120087GB-C21, and in part by the Ministry of Science, Technology and Innovation of Colombia under scholarship programme 885. ; González, E.; Sanchís Saez, J.; Salcedo-Romero-De-Ávila, J.; Martínez Iranzo, MA. (2023). Conditional scenario-based model predictive control. Journal of the Franklin Institute. 360(10):6880-6905. https://doi.org/10.1016/j.jfranklin.2023.05.012 ; 6880 ; 6905 ; 360 ; 10