Pan, GuanruGuanruPanFaulwasser, TimmTimmFaulwasser2024-02-142024-02-142023-01Automatica 147: 110708 (2023-01)https://hdl.handle.net/11420/45645Dimensionality reduction of decision variables is a practical and classic method to reduce the computational burden in linear and Nonlinear Model Predictive Control (NMPC). Available results range from early move-blocking ideas to singular-value decomposition. For schemes more complex than move-blocking it is seemingly not straightforward to guarantee recursive feasibility of the receding-horizon optimization. Decomposing the space of decision variables related to the inputs into active and inactive complements, this paper proposes a general framework for effective feasibility-preserving dimensionality reduction in NMPC. We show how – independently of the actual choice of the subspaces – recursive feasibility can be established. Moreover, we propose the use of global sensitivity analysis to construct the active subspace in data-driven fashion based on user-defined criteria. Numerical examples illustrate the efficacy of the proposed scheme. Specifically, for a chemical reactor we obtain a significant reduction by factor 20−40 at a closed-loop performance decay of less than 0.05%.en0005-10982023ElsevierActive subspacesDimensionality reductionGlobal sensitivity analysisNonlinear model predictive controlReduced-order MPCPhysicsJournal Article10.1016/j.automatica.2022.110708Journal Article