|Table of Contents|

Zone predictive control based on soft constraint adjustment method

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2014年03期
Page:
332-
Research Field:
Publishing date:

Info

Title:
Zone predictive control based on soft constraint adjustment method
Author(s):
Li Qi’anShang YueLi Yue
School of Information and Control Engineering,Liaoning Shihua University,Fushun 113001,China
Keywords:
soft constraint adjustment methodpredictive controlzone controlcomplicated industrial process controlzone constraintsmultivariable predictive controlquality variablesnormal constraint variablesweightslinear quadratic Gaussian methodinteraction matrix calculations
PACS:
TP273
DOI:
-
Abstract:
A soft constraint adjustment method is developed for the multivariable predictive control problem with zone constraint requirements in complicated industrial process control.All output variables are grouped into two categories,namely quality variables and normal constraint variables,according to different control requirements.The variable weights are chosen according to engineering experience.In order to further improve the control performance,the linear quadratic Gaussian method is introduced into the soft constraint adjustment method.The simulation results show that the proposed algorithm doesn’t need to estimate whether the output variables stay within the zone control requirements and avoids complicated interaction matrix calculations,which can satisfy the actual industrial needs more easily.

References:

[1]Perez T,Goodwin G C.Constrained predictive control of ship fin stabilizers to prevent dynamic stall[J].Control Engineering Practice,2008,16(4):482-494.
[2]Maldonado M,Desbiens A R,del Villar R.Potential use of model predictive control for optimizing the column flotation process[J]. International Journal ofMineral Processing,2009,93(1):26-33.
[3]Vesely V,Rosinová D,Foltin M.Robust model predictive control design with input constraints[J].ISA Transactions,2010,49(1):114-120.
[4]González A H,Odloak D.A stable MPC with zone control[J].Journal of Process Control,2009,19(1):110-122.
[5]商富荣.区间预测控制算法研究及稳定性分析[D].青岛:中国石油大学信息与控制工程学院,2008.
[6]Bartlett R A,Biegler L T,Backstrom J,et al.Quadratic programming algorithms for largescale model predictive control[J].Journal of Process Control,2002,12(7):775-795.
[7]徐祖华,赵均,钱积新.基于软约束方法的区间预测控制[J].机床与液压,2004(3):106-108.
Xu Zuhua,Zhao Jun,Qian Jixin.Zone model predictive control algorithm using soft constraint method[J].Machine Tool & Hydraulics,2004(3):106-108.
[8]邹涛,李海强.具有积分环节多变量系统的双层结构预测控制[J].浙江大学学报(工学版),2011,45(12):2079-2087,2195.
Zou Tao,Li Haiqiang.Twolayer predictive control of multivariable system with integrating element[J].Journal of Zhejiang University(Engineering Science),2011,45(12):2079-2087,2195.
[9]Alvarez L A,Odloak D.Robust integration of real time optimization with linear model predictive control[J].Computers & Chemical Engineering,2010,34(12):1937-1944.
[10]awryńczuk M.Online setpoint optimisation and predictive control using neural Hammerstein models[J].Chemical Engineering Journal,2011,166(1):269-287.
[11]赵超,张登峰,许巧玲,等.基于加权偏离度统计方法的预测控制性能评估算法[J].化工学报,2012,63(12):3971-3977.
Zhao Chao,Zhang Dengfeng,Xu Qiaoling,et al.Control performance assessment of model predictive control based on statistics analysis of weighted points[J].CIESC Journal,2012,63(12):3971-3977.
[12]周猛飞,潘海天,蔡亦军,等.基于最小方差基准的软约束调整与经济性能协调[J].计算机与应用化学,2012,29(2):181-184.
Zhou Mengfei,Pan Haitian,Cai Yijun,et al.Constraints adjustment and economic performance coordination based minimum variance benchmark[J].Computers and Applied Chemistry,2012,29(2):181-184.
[13]Prett D M,Morari M.The shell process control workshop[M].Boston,USA:Butterworths,1987:20-31.

Memo

Memo:
-
Last Update: 2014-06-30