引用本文:李世卿,丁宝苍,孙耀.双层预测控制中基于操作变量增量的多优先级稳态目标计算[J].控制理论与应用,2015,32(2):239~245.[点击复制]
LI Shi-qing,DING Bao-cang,SUN Yao.Multi-priority rank steady-state target calculation in double-layered model predictive control by optimizing increments of manipulated variables[J].Control Theory and Technology,2015,32(2):239~245.[点击复制]
双层预测控制中基于操作变量增量的多优先级稳态目标计算
Multi-priority rank steady-state target calculation in double-layered model predictive control by optimizing increments of manipulated variables
摘要点击 3035  全文点击 2362  投稿时间:2014-03-15  修订日期:2014-08-16
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DOI编号  10.7641/CTA.2015.40207
  2015,32(2):239-245
中文关键词  预测控制  多优先级策略  稳态目标计算
英文关键词  model predictive control (MPC)  multi-priority rank strategy  steady-state target calculation (SSTC)
基金项目  国家高技术研究发展计划(863计划)项目(2014AA041802), 国家自然科学基金项目(61174095)资助.
作者单位E-mail
李世卿 西安交通大学 电子与信息工程学院 自动化系 lsqjhy@126.com 
丁宝苍* 西安交通大学 电子与信息工程学院 自动化系 baocangding@126.com 
孙耀 西安交通大学 电子与信息工程学院 自动化系  
中文摘要
      本文给出一种双层结构预测控制(MPC)中多优先级稳态目标计算(SSTC)的描述方法. 在可行性阶段, 被控变量(CV)和外部目标(ET)的软约束以及操作变量(MV)的硬约束被统一表述为关于MV增量的约束, 将软约束(包括ET的期望上下界、CV的操作上下界、以及ET的跟踪)进行放松, 保证放松以后MV增量约束集的相容性. 在经济优化阶段, 在MV增量约束集中寻找经济最优的MV增量值. 该算法在已有文献的基础上, 对ET/CV的等式/不等式约束统一处理. 仿真算例证实了该算法的有效性.
英文摘要
      We give a description of the multi-priority rank steady-state target calculation (SSTC) in the double-layered model predictive control (MPC). In the feasibility stage, the soft constraints of the controlled variables (CVs) and the external targets (ETs), and the hard constraints of manipulated variables (MVs), are represented uniformly as the constraints on the MV increments. Then, the soft constraints (including the desired bounds of ETs, the operating constraints of CVs, and tracking of ETs), are slackened in order to guarantee the compatibility of constraints on MV increments. In the economics stage, the economically optimal increments of MVs are searched in their constraint set. This algorithm unifies the treatment of equality/inequality constraints of CVs/ETs, based on the exiting literature. The numerical example validates the effectiveness of the proposed algorithm.