A Scenario-Based Convex Formulation for Probabilistic Linear Constraints in MPC

被引:0
|
作者
Li, Jiwei [1 ]
Li, Dewei
Xi, Yugeng
机构
[1] Shanghai Jiao Tong Univ, Dept Automat, Shanghai, Peoples R China
来源
2015 10TH ASIAN CONTROL CONFERENCE (ASCC) | 2015年
关键词
MODEL-PREDICTIVE CONTROL; RANDOMIZED SOLUTIONS; SYSTEMS;
D O I
暂无
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 develops a model predictive control strategy for stochastic linear systems with both multiplicative and additive uncertainty. As satisfaction of probabilistic constraints as well as performance optimization relies on description of the random system nature, we derive polyhedrons that contain system evolution matrices with prescribed probability. This is achieved by letting each polyhedron incorporates a number of stochastic scenarios of the corresponding evolution matrix. The process is efficient through a designed convex optimization and subsequent off-line scaling and verification. On the basis of the polyhedrons, probabilistic constraints can be transformed into linear constraints and be solved in reduced computation burden. The proposed MPC algorithm ensures the constraints and closed loop stability. The results are illustrated by a numerical example.
引用
收藏
页数:6
相关论文
共 50 条
  • [21] Scenario-Based Hierarchical and Distributed MPC for Water Resources Management with Dynamical Uncertainty
    P. Velarde
    X. Tian
    A. D. Sadowska
    J. M. Maestre
    Water Resources Management, 2019, 33 : 677 - 696
  • [22] Scenario-Based Hierarchical and Distributed MPC for Water Resources Management with Dynamical Uncertainty
    Velarde, P.
    Tian, X.
    Sadowska, A. D.
    Maestre, J. M.
    WATER RESOURCES MANAGEMENT, 2019, 33 (02) : 677 - 696
  • [23] Scenario-based stochastic MPC for vehicle speed control considering the interaction with pedestrians
    Anh-Tuan Tran
    Muraleedharan, Arun
    Okuda, Hiroyuki
    Suzuki, Tatsuya
    IFAC PAPERSONLINE, 2020, 53 (02): : 15325 - 15331
  • [24] Scenario-Based Methods for Interval Linear Programming Problems
    Cao, M. F.
    Huang, G. H.
    JOURNAL OF ENVIRONMENTAL INFORMATICS, 2011, 17 (02) : 65 - 74
  • [25] Scenario-Based Approach to Stochastic Linear Predictive Control
    Matusko, Jadranko
    Borrelli, Francesco
    2012 IEEE 51ST ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2012, : 5194 - 5199
  • [26] Scenario-based Analysis of Potential and Constraints of Alkaline Electrochemical Cells
    Krewer, Ulrike
    Schroeder, Daniel
    Weinzierl, Christine
    24TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, PTS A AND B, 2014, 33 : 1237 - 1242
  • [27] Probabilistic feasibility guarantees for convex scenario programs with an arbitrary number of discarded constraints
    Romao, Licio
    Margellos, Kostas
    Papachristodoulou, Antonis
    AUTOMATICA, 2023, 149
  • [28] Scenario MPC for Linear Time-Varying Systems with Individual Chance Constraints
    Schildbach, Georg
    Morari, Manfred
    2015 AMERICAN CONTROL CONFERENCE (ACC), 2015, : 415 - 421
  • [29] A probabilistic procedure for scenario-based seismic hazard maps of Greater Tehran
    Yazdani, Azad
    Kowsari, Milad
    ENGINEERING GEOLOGY, 2017, 218 : 162 - 172
  • [30] Probabilistic Modeling of Multisite Wind Farm Production for Scenario-Based Applications
    Le, Duong D.
    Gross, George
    Berizzi, Alberto
    IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2015, 6 (03) : 748 - 758