Risk-averse model predictive control

被引：43

作者：

Sopasakis, Pantelis ^{[1
]}

Herceg, Domagoj ^{[2
]}

Bemporad, Alberto ^{[2
]}

Patrinos, Panagiotis ^{[3
]}

机构：

[1] Univ Cyprus, KIOS Res Ctr Intelligent Syst & Networks, 1 Panepistimiou Ave, CY-2109 Nicosia, Cyprus

[2] IMT Sch Adv Studies Lucca, Piazza San Francesco 19, I-55100 Lucca, Italy

[3] Katholieke Univ Leuven, Dept Elect Engn ESAT, STADIUS Ctr Dynam Syst Signal Proc & Data Analyt, Kasteelpk Arenberg 10, B-3001 Leuven, Belgium

来源：

AUTOMATICA | 2019年 / 100卷

关键词：

Risk measures; Nonlinear Markovian switching systems; Model predictive control; OPTIMIZATION;

D O I：

10.1016/j.automatica.2018.11.022

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Risk-averse model predictive control (MPC) offers a control framework that allows one to account for ambiguity in the knowledge of the underlying probability distribution and unifies stochastic and worst case MPC. In this paper we study risk-averse MPC problems for constrained nonlinear Markovian switching systems using generic cost functions, and derive Lyapunov-type risk-averse stability conditions by leveraging the properties of risk-averse dynamic programming operators. We propose a controller design procedure to design risk-averse stabilizing terminal conditions for constrained nonlinear Markovian switching systems. Lastly, we cast the resulting risk-averse optimal control problem in a favorable form which can be solved efficiently and thus deems risk-averse MPC suitable for applications. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：281 / 288

页数：8

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