Stochastic model predictive control for constrained discrete-time Markovian switching systems

被引：79

作者：

Patrinos, Panagiotis ^{[1
]}

Sopasakis, Pantelis ^{[1
]}

Sarimveis, Haralambos ^{[2
]}

Bemporad, Alberto ^{[1
]}

机构：

[1] IMT Inst Adv Studies Lucca, I-55100 Lucca, Italy

[2] Natl Tech Univ Athens, Sch Chem Engn, Athens 15780, Greece

来源：

AUTOMATICA | 2014年 / 50卷 / 10期

关键词：

Stochastic model predictive control; Control of constrained systems; Stochastic switching systems; LINEAR-SYSTEMS; FEEDBACK-CONTROL; STABILITY; OPTIMIZATION; STATE;

D O I：

10.1016/j.automatica.2014.08.031

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 study constrained stochastic optimal control problems for Markovian switching systems, an extension of Markovian jump linear systems (MILS), where the subsystems are allowed to be nonlinear. We develop appropriate notions of invariance and stability for such systems and provide terminal conditions for stochastic model predictive control (SMPC) that guarantee mean-square stability and robust constraint fulfillment of the Markovian switching system in closed-loop with the SMPC law under very weak assumptions. In the special but important case of constrained MILS we present an algorithm for computing explicitly the SMPC control law off-line, that combines dynamic programming with parametric piecewise quadratic optimization. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：2504 / 2514

页数：11

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