化工学报 ›› 2015, Vol. 66 ›› Issue (6): 2159-2165.DOI: 10.11949/j.issn.0438-1157.20141701

• 过程系统工程 • 上一篇    下一篇

基于三角区间软约束的模型预测控制算法

孙超, 戴睿, 郝晓辰, 刘彬, 周湛鹏   

  1. 燕山大学电气工程学院, 河北 秦皇岛 066004
  • 收稿日期:2014-11-18 修回日期:2015-03-10 出版日期:2015-06-05 发布日期:2015-03-11
  • 通讯作者: 戴睿
  • 基金资助:

    河北省科学技术研究与发展计划科技支撑计划项目(12215616D);中国博士后科学基金资助项目(2012M520596)。

A model predictive control algorithm based on triangle interval soft constraint

SUN Chao, DAI Rui, HAO Xiaochen, LIU Bin, ZHOU Zhanpeng   

  1. School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, Hebei, China
  • Received:2014-11-18 Revised:2015-03-10 Online:2015-06-05 Published:2015-03-11
  • Supported by:

    supported by the Science and Technology Support Program of Hebei Province (12215616D) and China Postdoctoral Science Foundation Funded Project (2012M520596).

摘要:

针对设定值控制多入多出(MIMO)系统在外界干扰下系统自由度低和鲁棒性差的问题, 提出了兼顾定值控制与鲁棒性的基于三角区间软约束的模型预测控制算法。文中算法在设定值控制的基础上增加了三角区间软约束, 使得控制目标分阶段达成, 以减小干扰对系统的影响, 提高系统自由度及鲁棒性。最后, 对算法的鲁棒性进行了分析, 并采用Shell公司的典型重油分馏塔进行仿真实验, 通过与设定值控制结果的对比, 证明了文中算法有更好的鲁棒性及更优良的控制品质。

关键词: 模型预测控制, 过程控制, 算法, 设定值控制, 区间控制, 软约束, 鲁棒性

Abstract:

The set point control strategy has low degree of freedom and bad robustness by the influence of outside interference. In order to resolve this problem and improve control quality, an improved algorithm, model predictive control algorithm based on triangular interval soft constraint, was presented. On the basis of set point control triangular interval soft constraints were added and system output reached the control objectives in stages, to reduce the influence of interference on the system and to improve degree of freedom and robustness. Finally, robustness of the algorithm was analyzed. Simulation experiment was done with the typical heavy oil fractionator model of Shell Company. Comparison with the results of set point control proved that the algorithm had better robustness and better control quality.

Key words: model-predictive control, process control, algorithm, set point control, interval control, soft constraint, robustness

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