多变量预测控制结构分解的图论方法
Graph theory method for multivariate predictive control structure decomposition
摘要点击 88  全文点击 42  投稿时间:2019-11-05  修订日期:2020-08-06
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DOI编号  10.7641/CTA.2020.90921
  2020,37(9):1904-1912
中文关键词  预测控制  图论  计算复杂度  系统分解
英文关键词  predictive control  graph theory  computational complexity: system decomposition
基金项目  国家自然科学基金项目(61773366);辽宁省自然基金(2019-KF-03-07);辽宁省博士启动基金 (20180540066);工信部工业互联网创新发展工程及智能制造综合标准化与新模式应用项目(时间敏感网络 (TSN)与用于工业控制的对象链接与嵌入统一架构(OPC UA)融合关键技术标准研究与试验验证).
作者单位E-mail
王洪瑞 中国科学院沈阳自动化研究所 wanghongrui@sia.cn 
邹涛 广州大学 zoutao@sia.cn 
张鑫 中国科学院沈阳自动化研究所  
王美聪 沈阳化工大学  
陆云松 中国科学院沈阳自动化研究所  
中文摘要
      预测控制算法的计算复杂度主要由变量个数和控制时域决定, 而大型复杂系统中变量个数较多将导致计 算量大的问题, 尤其在有约束预测控制的优化求解中增加较重的计算负担. 本文针对此问题利用邻接矩阵、可达矩 阵和关联矩阵梳理系统传递函数模型中变量之间的关联, 将有关联的控制变量划分为一个子系统, 进而将一个大系 统分解成若干独立子系统, 即可将一个高维度的优化求解问题分解成多个维度较低的子优化问题, 降低计算复杂度 以达到减少计算量的目的. 最后将其应用在多变量有约束的双层结构预测控制算法中, 通过仿真进行验证.
英文摘要
      The computational complexity of the model predictive control algorithm is principally determined by the number of variables and the control time domain. While substantial variables in large-scale complex system will result in the problem of a great deal of computation, in particularly increasing computing burden in the optimization solution of constrained predictive control algorithm. In allusion to the problem, this paper employs adjacency matrix, reachability matrix and correlation matrix to sort out the associations among variables in the transfer function model of the system so that the related control variables are divided into the same one subsystem. Then decompose a large-scale system into divers independent subsystems. And a high dimensional optimization problem will be decomposed into several low dimensional sub optimization problems in order that decrease the computational complexity and the calculation. In the end of the paper applies the method to the multivariable constrained double layered structure predictive control algorithm and verified by simulation, which provides a new idea for the multivariable decomposition method.