Soft‐constrained model predictive control based on data‐driven distributionally robust optimization

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This article proposes a novel distributionally robust optimization (DRO)-based soft-constrained model predictive control (MPC) framework to explicitly hedge against unknown external input terms in a linear state-space system. Without a priori knowledge of the exact uncertainty distribution, this framework works with a lifted ambiguity set constructed using machine learning to incorporate the first-order moment information. By adopting a linear performance measure and considering input and state constraints robustly with respect to a lifted support set, the DRO-based MPC is reformulated as a robust optimization problem. The constraints are softened to ensure recursive feasibility. Theoretical results on optimality, feasibility, and stability are further discussed. Performance and computational efficiency of the proposed method are illustrated through motion control and building energy control systems, showing 18.3% less cost and 78.8% less constraint violations, respectively, while requiring one third of the CPU time compared to multi-stage scenario based stochastic MPC.
Publisher
WILEY
Issue Date
2020-10
Language
English
Article Type
Article; Early Access
Citation

AICHE JOURNAL, v.66, no.10, pp.e16546

ISSN
0001-1541
DOI
10.1002/aic.16546
URI
http://hdl.handle.net/10203/276374
Appears in Collection
CBE-Journal Papers(저널논문)
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