A Constraint-Tightening Approach to Nonlinear Model Predictive Control with Chance Constraints for Stochastic Systems

被引：17

作者：

Santos, Tito L. M. ^{[1
]}

Bonzanini, Angelo D. ^{[2
]}

Heirung, Tor Aksel N. ^{[2
]}

Mesbah, Ali ^{[2
]}

机构：

[1] Univ Fed Bahia, Dept Elect Engn, BR-40210630 Salvador, BA, Brazil

[2] Univ Calif Berkeley, Dept Chem & Biomol Engn, Berkeley, CA 94720 USA

来源：

2019 AMERICAN CONTROL CONFERENCE (ACC) | 2019年

关键词：

STABILITY; TUBES; MPC;

D O I：

10.23919/acc.2019.8814623

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a nonlinear model predictive control (NMPC) strategy for stochastic systems subject to chance constraints. The notion of stochastic tubes is extended to nonlinear systems to present a constraint tightening strategy that ensures stability and recursive feasibility of NMPC in the presence of stochastic uncertainties. State constraints are tightened recursively by constructing a sequence of sets from an initial constraint set, which is tightened using constraint backoff parameters obtained from either the probability distribution or the empirical cumulative distribution of the uncertainties. The performance of the NMPC strategy with chance constraints is compared to that of robust NMPC on a DC-DC converter benchmark case study.

引用

页码：1641 / 1647

页数：7

共 50 条

[41] Distributed Model Predictive Control of Nonlinear Systems with Input Constraints
Liu, Jinfeng
de la Pena, David Munoz
Christofides, Panagiotis D.
[J]. 2009 AMERICAN CONTROL CONFERENCE, VOLS 1-9, 2009, : 2319 - +
[42] Robust model predictive control under chance constraints
Li, P
Wendt, M
Wozny, G
[J]. COMPUTERS & CHEMICAL ENGINEERING, 2000, 24 (2-7) : 829 - 834
[43] Nonlinear Model Predictive Control for Stochastic Differential Equation Systems
Brok, Niclas Laursen
Madsen, Henrik
Jorgensen, John Bagterp
[J]. IFAC PAPERSONLINE, 2018, 51 (20): : 430 - 435
[44] Output Feedback Model Predictive Control of Stochastic Nonlinear Systems
Homer, Tyler
Mhaskar, Prashant
[J]. 2015 AMERICAN CONTROL CONFERENCE (ACC), 2015, : 793 - 798
[45] Constraints Tightening Approach Towards Model Predictive Control Based Rendezvous and Docking with Uncooperative Targets
Iskender, O. B.
Ling, K., V
Dubanchet, V
[J]. 2018 EUROPEAN CONTROL CONFERENCE (ECC), 2018, : 381 - 386
[46] Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints
Cannon, Mark
Kouvaritakis, Basil
Wu, Xingjian
[J]. AUTOMATICA, 2009, 45 (01) : 167 - 172
[47] Convergence of Stochastic Nonlinear Systems and Implications for Stochastic Model-Predictive Control
Munoz-Carpintero, Diego
Cannon, Mark
[J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2021, 66 (06) : 2832 - 2839
[48] Linear model predictive control based on approximate optimal control inputs and constraint tightening
Necoara, Ion
Nedelcu, Valentin
Keviczky, Tamas
Minh Dang Doan
de Schutter, Bart
[J]. 2013 IEEE 52ND ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2013, : 7728 - 7733
[49] Chance Constrained Model Predictive Control for Multi-Agent Systems with Coupling Constraints
Lyons, Daniel
Calliess, Jan-P.
Hanebeck, Uwe D.
[J]. 2012 AMERICAN CONTROL CONFERENCE (ACC), 2012, : 1223 - 1230
[50] Model Predictive Control for Stochastic Max-Plus Linear Systems With Chance Constraints (vol 64, pg 337, 2019)
van den Boom, Ton J. J.
Xu, Jia
De Schutter, Bart
[J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2020, 65 (02) : 905 - 906

← 1 2 3 4 5 →