Coupling Degree Clustering-Based Distributed Model Predictive Control Network Design

被引:50
作者
Zheng, Yi [1 ]
Wei, Yongsong [1 ]
Li, Shaoyuan [1 ]
机构
[1] Shanghai Jiao Tong Univ, Dept Automat, Minist Educ China, Key Lab Syst Control & Informat Proc, Shanghai 200240, Peoples R China
关键词
Distributed model predictive control (DMPC); large-scale systems; model predictive control (MPC); plant-wide optimization; structure design; RECEDING HORIZON CONTROL; NONLINEAR-SYSTEMS; OPTIMIZATION; COMMUNICATION; DECOMPOSITION; COORDINATION; STABILITY; ALGORITHM; SUBJECT;
D O I
10.1109/TASE.2017.2780444
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Designing a stabilized distributed model predictive control (DMPC) with constraints is an open and important problem for a class of large-scale distributed systems, which are composed by both weakly and strongly coupled subsystems. This paper proposes a design of DMPC network to stabilize this class of large-scale systems. A coupling degree-based clustering method is first designed to classify subsystems into some middle-scale subsystems (M-subsystem) off-line according to the adjacent matrix, so that these M-subsystems are weakly coupled with each other. Then, each M-subsystem is controlled by a virtual model predictive control (MPC), which is realized by several individual controllers with running iterative cooperative DMPC algorithm, since the solution of cooperative DMPC is able to converge to a fixed point without coupling constraints. Each MPC communicates with the corresponding interacted M-subsystems' MPCs once in a control period for exchanging future state evolution estimation. All the subsystem-based MPCs are composed of the proposed peer-to-peer DMPC network. In addition, an additional consistency and stabilization constraints are added to guarantee the recursive feasibility and stability of the overall system. The convergence of the iterative DMPC algorithm for each M-subsystem and the stabilization analysis of the overall system are provided. The simulation results show the efficiency of the proposed method.
引用
收藏
页码:1749 / 1758
页数:10
相关论文
共 38 条
  • [1] A robust distributed model predictive control based on a dual-mode approach
    Al-Gherwi, Walid
    Budman, Hector
    Elkamel, Ali
    [J]. COMPUTERS & CHEMICAL ENGINEERING, 2013, 50 : 130 - 138
  • [2] A robust distributed model predictive control algorithm
    Al-Gherwi, Walid
    Budman, Hector
    Elkamel, Ali
    [J]. JOURNAL OF PROCESS CONTROL, 2011, 21 (08) : 1127 - 1137
  • [3] Selection of control structure for distributed model predictive control in the presence of model errors
    Al-Gherwi, Walid
    Budman, Hector
    Elkamel, Ali
    [J]. JOURNAL OF PROCESS CONTROL, 2010, 20 (03) : 270 - 284
  • [4] ON LINEAR-PROGRAMMING AND ROBUST MODEL-PREDICTIVE CONTROL USING IMPULSE-RESPONSES
    ALLWRIGHT, JC
    PAPAVASILIOU, GC
    [J]. SYSTEMS & CONTROL LETTERS, 1992, 18 (02) : 159 - 164
  • [5] [Anonymous], 2002, Predictive Control: With Constraints
  • [6] [Anonymous], 2011, DECENTRALIZED CONTRO
  • [7] Distributed model predictive control
    Camponogara, Eduardo
    Jia, Dong
    Krogh, Bruce H.
    Talukdar, Sarosh
    [J]. IEEE Control Systems Magazine, 2002, 22 (01): : 44 - 52
  • [8] Distributed Optimization for Model Predictive Control of Linear Dynamic Networks With Control-Input and Output Constraints
    Camponogara, Eduardo
    Scherer, Helton F.
    [J]. IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2011, 8 (01) : 233 - 242
  • [9] Dantzig-Wolfe decomposition and plant-wide MPC coordination
    Cheng, Ruoyu
    Forbes, J. Fraser
    Yip, W. San
    [J]. COMPUTERS & CHEMICAL ENGINEERING, 2008, 32 (07) : 1507 - 1522
  • [10] Distributed model predictive control: A tutorial review and future research directions
    Christofides, Panagiotis D.
    Scattolini, Riccardo
    Munoz de la Pena, David
    Liu, Jinfeng
    [J]. COMPUTERS & CHEMICAL ENGINEERING, 2013, 51 : 21 - 41