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NMPC in active subspaces: Dimensionality reduction with recursive feasibility guarantees
Publikationstyp
Journal Article
Date Issued
2023-01
Sprache
English
Author(s)
Pan, Guanru
Journal
Volume
147
Article Number
110708
Citation
Automatica 147: 110708 (2023-01)
Publisher DOI
Scopus ID
Publisher
Elsevier
ISSN
0005109
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%.
Subjects
Active subspaces
Dimensionality reduction
Global sensitivity analysis
Nonlinear model predictive control
Reduced-order MPC
DDC Class
530: Physics