Stochastic model predictive control - how does it work?

被引：120
作者：
Heirung, Tor Aksel N. [1 ]
Paulson, Joel A. [1 ]
O'Leary, Jared [1 ]
Mesbah, Ali [1 ]
机构：
[1] Univ Calif Berkeley, Dept Chem & Biomol Engn, Berkeley, CA 94720 USA
来源：
COMPUTERS & CHEMICAL ENGINEERING | 2018年 / 114卷
关键词：
Model predictive control; Stochastic optimal control; Chance constraints; Disturbance modeling; RECEDING HORIZON CONTROL; LEAST-SQUARES METHOD; OPTIMIZATION; SYSTEMS; STATE; IDENTIFICATION; SEPARATION; FEEDBACK;
D O I：
10.1016/j.compchemeng.2017.10.026
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Stochastic model predictive control (SMPC) provides a probabilistic framework for MPC of systems with stochastic uncertainty. A key feature of SMPC is the inclusion of chance constraints, which enables a systematic trade-off between attainable control performance and probability of state constraint violations in a stochastic setting. This paper presents an overview of core concepts in SMPC in relation to MPC and stochastic optimal control, with numerical illustrations on a typical chemical process. Estimation of stochastic disturbances as well as the impact of estimation quality of stochastic disturbances on the SMPC performance are discussed. Some avenues for future research in SMPC are suggested. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码：158 / 170
页数：13
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