An outlook on robust model predictive control algorithms: Reflections on performance and computational aspects

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Abstract

In this paper, we discuss the model predictive control algorithms that are tailored for uncertain systems. Robustness notions with respect to both deterministic (or set based) and stochastic uncertainties are discussed and contributions are reviewed in the model predictive control literature. We present, classify and compare different notions of the robustness properties of state of the art algorithms, while a substantial emphasis is given to the closed-loop performance and computational complexity properties. Furthermore, connections between (i) the theory of risk and (ii) robust optimization research areas and robust model predictive control are discussed. Lastly, we provide a comparison of current robust model predictive control algorithms via simulation examples illustrating closed loop performance and computational complexity features.

Original languageEnglish
Pages (from-to)77-102
Number of pages26
JournalJournal of Process Control
Volume61
DOIs
Publication statusPublished - 1 Jan 2018

Keywords

  • Computational complexity
  • Model predictive control
  • Risk mappings
  • Robust optimization
  • Robustness
  • Uncertainty descriptions

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