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Cited by 156Advanced Control Systems OptimizationRisk and Portfolio OptimizationStability and Control of Uncertain Systems

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A Distributionally Robust Optimization Based Method for Stochastic Model Predictive Control

Li, Bin, Tan, Yuan, Wu, Ai-Guo, Duan, Guang-Ren·2022·IEEE Transactions on Automatic Control
DOI 10.1109/tac.2021.3124750
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Abstract

Two stochastic model predictive control algorithms, which are referred to as distributionally robust model predictive control algorithms, are proposed in this article for a class of discrete linear systems with unbounded noise. Participially, chance constraints are imposed on both of the state and the control, which makes the problem more challenging. Inspired by the ideas from distributionally robust optimization (DRO), two deterministic convex reformulations are proposed for tackling the chance constraints. Rigorous computational complexity analysis is carried out to compare the two proposed algorithms with the existing methods. Recursive feasibility and convergence are proven. Simulation results are provided to show the effectiveness of the proposed algorithms.

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Citations

Cites

Research works printed but not linked: 37

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Metadata

Authors
Li, B., Tan, Y., Wu, A.-G., & Duan, G.-R.
Affiliations
  • Sichuan University (CN)
  • Southeast University (CN)
  • Harbin Institute of Technology (CN)
ORCID
Venue
IEEE Transactions on Automatic Control
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2022-11
Volume
67
Issue
11
Pages
5762-5776
ISSN
  • 0018-9286 (ISSN-L)
  • 1558-2523
  • 2334-3303
DOI
10.1109/tac.2021.3124750
OpenAlex
W3209358401
Cited by
156
References
1
Citations by year
2026: 24 · 2025: 30 · 2024: 27 · 2023: 71 · 2022: 4
FWCI
11.35
Type
journal article
Language
en
Open access
closed
Licence
Locations
IEEE Transactions on Automatic Control (publishedVersion)
Funders
  • National Natural Science Foundation of China (62071317)
  • National Natural Science Foundation of China for Excellent Young Scholars (61822305)
  • Science and Technology on Space Intelligent Control Laboratory (KGJZDSYS-2018-03)
  • Natural Science Foundation of Guangdong Province (2019A1515011576)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Risk and Portfolio Optimization
  • Physical Sciences › Engineering › Control and Systems Engineering › Stability and Control of Uncertain Systems
Keywords
stochastic model predictive control, distributionally robust optimization, chance constraints, discrete-time linear systems, convex reformulation, complexity analysis, recursive feasibility, convergence analysis

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