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

页码：158 / 170

页数：13

共 69 条

[21] Stochastic linear Model Predictive Control with chance constraints - A review [J].

Farina, Marcello ;

Giulioni, Luca ;

Scattolini, Riccardo .

JOURNAL OF PROCESS CONTROL, 2016, 44 :53-67

[22] Model Predictive Path-Following for Constrained Nonlinear Systems [J].

Faulwasser, T. ;

Kern, B. ;

Findeisen, R. .

PROCEEDINGS OF THE 48TH IEEE CONFERENCE ON DECISION AND CONTROL, 2009 HELD JOINTLY WITH THE 2009 28TH CHINESE CONTROL CONFERENCE (CDC/CCC 2009), 2009, :8642-8647

[23] Optimization over state feedback policies for robust control with constraints [J].

Goulart, PJ ;

Kerrigan, EC ;

Maciejowski, JA .

AUTOMATICA, 2006, 42 (04) :523-533

[24] Stochastic receding horizon control with output feedback and bounded controls [J].

Hokayem, Peter ;

Cinquemani, Eugenio ;

Chatterjee, Debasish ;

Ramponi, Federico ;

Lygeros, John .

AUTOMATICA, 2012, 48 (01) :77-88

[25]

IEEE, 1971, IEEE T AUTOM CONTROL, V16

[26]

Kalman R., 1960, Journal of Basic Engineering, V82, P35, DOI [DOI 10.1115/1.3662552, 10.1115/1.3662552]

[27]

Kouvaritakis B, 2016, ADV TXB CONTR SIG PR, P1, DOI 10.1007/978-3-319-24853-0

[28] Developments in Robust and Stochastic Predictive Control in the Presence of Uncertainty [J].

Kouvaritakis, B. ;

Cannon, M. .

ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART B-MECHANICAL ENGINEERING, 2015, 1 (02)

[29] Explicit use of probabilistic distributions in linear predictive control [J].

Kouvaritakis, Basil ;

Cannon, Mark ;

Rakovic, Sasa V. ;

Cheng, Qifeng .

AUTOMATICA, 2010, 46 (10) :1719-1724

[30]

Lewis F.L., 2008, Optimal and Robust Estimation: With an Introduction to Stochastic Control Theory

← 1 2 3 4 5 6 7 →