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• W4308984485 abstract "Dimensionality 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 $0.05%$." @default.
• W4308984485 created "2022-11-20" @default.
• W4308984485 creator A5054359566 @default.
• W4308984485 creator A5085075201 @default.
• W4308984485 date "2023-01-01" @default.
• W4308984485 modified "2023-10-03" @default.
• W4308984485 title "NMPC in active subspaces: Dimensionality reduction with recursive feasibility guarantees" @default.
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• W4308984485 doi "https://doi.org/10.1016/j.automatica.2022.110708" @default.
• W4308984485 hasPublicationYear "2023" @default.
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