Tube-based Stochastic Nonlinear Model Predictive Control: A Comparative Study on Constraint Tightening

被引:7
|
作者
Bonzanini, Angelo D. [1 ]
Santos, Tito L. M. [2 ]
Mesbah, Ali [1 ]
机构
[1] Univ Calif Berkeley, Dept Chem & Biomol Engn, Berkeley, CA 94720 USA
[2] Univ Fed Bahia, Dept Elect Engn, BR-40210630 Salvador, BA, Brazil
来源
IFAC PAPERSONLINE | 2019年 / 52卷 / 01期
关键词
Tube-based MPC; chance constraints; constraint tightening; incremental stabilizability; SYSTEMS; MPC;
D O I
10.1016/j.ifacol.2019.06.128
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 comparative study between two constraint-tightening approaches for tube-based stochastic nonlinear model predictive control (SNMPC) with and without terminal constraints. A simple constraint-tightening method based on the exponential decay rate of a (5-Lyapunov function is extended to the stochastic setting. This method uses the notion of incremental stabilizability to alleviate the need for offline, but involved computation of terminal constraints. The proposed method is compared to a SNMPC formulation that employs terminal constraints and Lipschitz constant-based constraint tightening. A comparative analysis is presented on a benchmark continuous stirred-tank reactor problem. Practical approximations for computing terminal sets are discussed in the context of this comparison. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码:598 / 603
页数:6
相关论文
共 50 条
  • [1] Accelerating tube-based model predictive control by constraint removal
    Jost, Michael
    Pannocchia, Gabriele
    Moennigmann, Martin
    [J]. 2015 54TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2015, : 3651 - 3656
  • [2] Tube-based robust nonlinear model predictive control
    Mayne, D. Q.
    Kerrigan, E. C.
    van Wyk, E. J.
    Falugi, P.
    [J]. INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2011, 21 (11) : 1341 - 1353
  • [3] Maximal Admissible Disturbance Constraint Set for Tube-Based Model Predictive Control
    Xie, Huahui
    Dai, Li
    Sun, Zhongqi
    Xia, Yuanqing
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2023, 68 (11) : 6773 - 6780
  • [4] Tube-based model predictive control for nonlinear systems with unstructured uncertainty
    Falugi, P.
    Mayne, D. Q.
    [J]. 2011 50TH IEEE CONFERENCE ON DECISION AND CONTROL AND EUROPEAN CONTROL CONFERENCE (CDC-ECC), 2011, : 2656 - 2661
  • [5] Constraint-Tightening and Stability in Stochastic Model Predictive Control
    Lorenzen, Matthias
    Dabbene, Fabrizio
    Tempo, Roberto
    Allgoewer, Frank
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2017, 62 (07) : 3165 - 3177
  • [6] Tube-Based Model Predictive Full Containment Control for Stochastic Multiagent Systems
    Li, Liya
    Shi, Peng
    Ahn, Choon Ki
    Kim, Yeong Jun
    Xing, Wen
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2023, 68 (07) : 4024 - 4037
  • [7] A Constraint-Tightening Approach to Nonlinear Model Predictive Control with Chance Constraints for Stochastic Systems
    Santos, Tito L. M.
    Bonzanini, Angelo D.
    Heirung, Tor Aksel N.
    Mesbah, Ali
    [J]. 2019 AMERICAN CONTROL CONFERENCE (ACC), 2019, : 1641 - 1647
  • [8] Fault-Tolerant Tube-Based Robust Nonlinear Model Predictive Control
    Paulson, Joel A.
    Heirung, Tor Aksel N.
    Mesbah, Ali
    [J]. 2019 AMERICAN CONTROL CONFERENCE (ACC), 2019, : 1648 - 1654
  • [9] Decentralized tube-based model predictive control of uncertain nonlinear multiagent systems
    Nikou, Alexandros
    Dimarogonas, Dimos V.
    [J]. INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2019, 29 (10) : 2799 - 2818
  • [10] Data-Driven Tube-Based Stochastic Predictive Control
    Kerz, Sebastian
    Teutsch, Johannes
    Brudigam, T.I.M.
    Leibold, Marion
    Wollherr, Dirk
    [J]. IEEE Open Journal of Control Systems, 2023, 2 : 185 - 199