An outlook on robust model predictive control algorithms: Reflections on performance and computational aspects. (January 2018)

• Record Type: Journal Article
• Authors: Saltık, M. Bahadır; Özkan, Leyla; Ludlage, Jobert H.A.; Weiland, Siep; Van den Hof, Paul M.J.
• Is Part Of: Journal of process control. Volume 61(2018)
• Page Start: 77
• Page End: 102
• Publication Date: 2018-01
• Subjects: Model predictive control -- Uncertainty descriptions -- Robustness -- Risk mappings -- Computational complexity -- Robust optimization; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2017.10.006
• Languages: English
• ISSNs: 0959-1524

Abstract

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.