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

被引：88

作者：

Saltik, M. Bahadir ^{[1
]}

Ozkan, Leyla ^{[1
]}

Ludlage, Jobert H. A. ^{[1
]}

Weiland, Siep ^{[1
]}

Van den Hof, Paul M. J. ^{[1
]}

机构：

[1] Eindhoven Univ Technol, Elect Engn Dept, NL-5612 AJ Eindhoven, Netherlands

来源：

JOURNAL OF PROCESS CONTROL | 2018年 / 61卷

关键词：

Model predictive control; Uncertainty descriptions; Robustness; Risk mappings; Computational complexity; Robust optimization; RECEDING HORIZON CONTROL; TO-STATE STABILITY; MULTIPLICATIVE UNBOUNDED UNCERTAINTY; CONSTRAINED LINEAR-SYSTEMS; OUTPUT-FEEDBACK MPC; STOCHASTIC TUBE MPC; NONLINEAR-SYSTEMS; INVARIANT-SETS; LPV SYSTEMS; MONTE-CARLO;

D O I：

10.1016/j.jprocont.2017.10.006

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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. (C) 2017 Elsevier Ltd. All rights reserved.

引用

页码：77 / 102

页数：26

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