基于优先级策略的模型预测控制性能评估
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摘要
为了解决基于函数加权法进行模型预测控制(MPC)潜在控制性能以及经济性能评估过程中出现的权重缺乏统一度量标准的问题,并考虑到实际操作中所考虑的安全平稳、产品质量、节能减排等目标具有不同的优先级的特点,提出了一种基于优先级策略的模型预测控制性能评估方法.对集中式MPC控制系统建模,基于线性二次最优控制(LQG)基准,将优先级引入控制目标,提出了优先级LQG基准,给出MPC参考参数,并通过稳态目标计算进行可行性判定,确定可行解空间,从而实现卡边控制,最大化经济效益.案例证明了该方法的有效性以及针对不同控制目标的灵活性.
During the past decades,various model predictive control(MPC)performance assessment methods were developed for the evaluation of the control and economic performance of MPC.In these methods,different objectives(e.g.,safety,product quality,and economics)in general were taken into account in one weighted objective function.The tuning of the weights for different objectives,however,is not a trivial task and still remains an open question.Moreover,due to practical concerns such as safety,product quality,economic,etc.,one objective may have total superiority over another.In this paper,aprioritized benchmark based on linear quadratic Gaussian(LQG)benchmarks was developed and applied to the economic performance assessment of MPC with prioritized objectives.The proposed approach was shown to provide better assessment result and was flexible in the assessment of different kinds of control objectives.
During the past decades,various model predictive control(MPC)performance assessment methods were developed for the evaluation of the control and economic performance of MPC.In these methods,different objectives(e.g.,safety,product quality,and economics)in general were taken into account in one weighted objective function.The tuning of the weights for different objectives,however,is not a trivial task and still remains an open question.Moreover,due to practical concerns such as safety,product quality,economic,etc.,one objective may have total superiority over another.In this paper,aprioritized benchmark based on linear quadratic Gaussian(LQG)benchmarks was developed and applied to the economic performance assessment of MPC with prioritized objectives.The proposed approach was shown to provide better assessment result and was flexible in the assessment of different kinds of control objectives.
引文
[1]邹涛,丁宝苍,张端.模型预测控制工程应用导论[M].北京:化学工业出版社,2010.
[2]Edgar T F.Control and operations:When does controllability equal profitability?[J].Computers&Chemical Engineering,2004,29(1):41-49.
[3]刘詟,苏宏业,谢磊,等.基于线性二次型高斯基准的多变量预测控制技术经济性能评估[J].控制理论与应用,2012,29(012):1530-1536.LIU Zhe,SU Hong-ye,XIE Lei,et al.Economic performance assessment of multivariable model predictive control based on linear quadratic Gaussian benchmark[J].Control Theory&Applications,2012,29(012):1530-1536.
[4]Xu F,Huang B,Akande S.Performance assessment of model predictive control for variability and constraint tuning[J].Industrial&Engineering Chemistry Research,2007,46(4):1208-1219.
[5]Harris T J,Boudreau F,MacGregor J F.Performance assessment of multivariable feedback controllers[J].Automatica,1996,32(11):1505-1518.
[6]Qin S J,Badgwell T A.A survey of industrial model predictive control technology[J].Control Engineering Practice,2003,11(7):733-764.
[7]Huang B,Shah S L.Performance assessment of control loops:Theory and applications[M].London:Springer,1999.
[8]Schfer J,Cinar A.Multivariable MPC system performance assessment,monitoring,and diagnosis[J].Journal of Process Control,2004,14(2):113-129.
[9]Zhao C,Zhao Y,Su H,et al.Economic performance assessment of advanced process control with LQG benchmarking[J].Journal of Process Control,2009,19(4):557-569.
[10]Liu S,Liu J,Feng Y,et al.Performance assessment of decentralized control systems:An iterative approach[J].Control Engineering Practice,2014,22:252-263.
[11]Grimble M J.Controller performance benchmarking and tuning using generalized minimum variance control[J].Automatica,2002,38(12):2111-2119.
[12]Gao J,Patwardhan R,Akamatsu K,et al.Performance evaluation of two industrial MPC controllers[J].Control Engineering Practice,2003,11(12):1371-1387.
[13]Ko B S,Edgar T F.Assessment of achievable PI control performance for linear processes with dead time[C]∥Proceedings of the American Control Conference.Pennsylvania USA:IEEE,1998:1548-1552.
[14]钱积新,赵均,徐祖华.预测控制[M].北京:化学工业出版社,2007:1-3.
[15]田智,吴刚.模块多变量预测控制[C]∥中国控制会议论文集.安徽省黄山市:中国自动化学会控制理论专业委员会,1995:642-648.
[16]Tao Z,Gang W,De-Feng H.Nonlinear model predictive control based on lexicographic multi-objective genetic algorithm[C]∥International Conference on Intelligent and Advanced Systems 2007.[s.l.]:IEEE,2007:61-65.
[17]Kerrigan E,Maciejowshi J.Designing model predictive controllers with prioritised constraints and objectives[C]∥Proc of IEEE Int Symposium on ComputerAided Control System Design.Glasgov:IEEE,2002:33-38.
[18]何德峰,宋秀兰,黄骅.优先级多目标稳定化约束模型预测控制[J].控制与决策,2013,28(12):1831-1836.HE De-feng,SONG Xiu-lan,HUANG Hua.Stabilizing constrained model predictive control with prioritized multi-objectives[J].Control and Decision,2013,28(12):1831-1836.
[19]李少远,席裕庚.模糊动态环境下复杂系统的满意优化控制[J].自动化学报,2002,28(3):408-412.LI Shao-yuan,XI Yu-geng.Satisfactory optimization control of complex systems in fuzzy dynamic environment[J].Acta Automatica Sinica,2002,28(3):408-412.
[20]Muske K R,Finegan C S.Analysis of a class of statistical techniques for estimating the economic benefit from improved process control[C]∥AIChE Symposium Series.New York:American Institute of Chemical Engineers,2002:408-412.
[21]徐玖平,李军.多目标决策的理论与方法[M].北京:清华大学出版社,2005.
[22]席裕庚,谷寒雨.有约束多目标多自由度优化的可行性分析及软约束调整[J].自动化学报,1998.24(6):727-732.XI Yu-geng,GU Han-yu.Feasibility analysis and soft constraints adjustment of CMMO[J].Acta Automatica Sinica,1998.24(6):727-732.
[23]De Oliveira M C,Geromel J C,Bernussou J.Extended H2and H norm characterizations and controller parameterizations for discrete-time systems[J].International Journal of Control,2002,75(9):666-679.
[24]Liu J,Muoz de la Pea D,Christofides P D.Distributed model predictive control of nonlinear process systems[J].AIChE Journal,2009,55(5):1171-1184.
[2]Edgar T F.Control and operations:When does controllability equal profitability?[J].Computers&Chemical Engineering,2004,29(1):41-49.
[3]刘詟,苏宏业,谢磊,等.基于线性二次型高斯基准的多变量预测控制技术经济性能评估[J].控制理论与应用,2012,29(012):1530-1536.LIU Zhe,SU Hong-ye,XIE Lei,et al.Economic performance assessment of multivariable model predictive control based on linear quadratic Gaussian benchmark[J].Control Theory&Applications,2012,29(012):1530-1536.
[4]Xu F,Huang B,Akande S.Performance assessment of model predictive control for variability and constraint tuning[J].Industrial&Engineering Chemistry Research,2007,46(4):1208-1219.
[5]Harris T J,Boudreau F,MacGregor J F.Performance assessment of multivariable feedback controllers[J].Automatica,1996,32(11):1505-1518.
[6]Qin S J,Badgwell T A.A survey of industrial model predictive control technology[J].Control Engineering Practice,2003,11(7):733-764.
[7]Huang B,Shah S L.Performance assessment of control loops:Theory and applications[M].London:Springer,1999.
[8]Schfer J,Cinar A.Multivariable MPC system performance assessment,monitoring,and diagnosis[J].Journal of Process Control,2004,14(2):113-129.
[9]Zhao C,Zhao Y,Su H,et al.Economic performance assessment of advanced process control with LQG benchmarking[J].Journal of Process Control,2009,19(4):557-569.
[10]Liu S,Liu J,Feng Y,et al.Performance assessment of decentralized control systems:An iterative approach[J].Control Engineering Practice,2014,22:252-263.
[11]Grimble M J.Controller performance benchmarking and tuning using generalized minimum variance control[J].Automatica,2002,38(12):2111-2119.
[12]Gao J,Patwardhan R,Akamatsu K,et al.Performance evaluation of two industrial MPC controllers[J].Control Engineering Practice,2003,11(12):1371-1387.
[13]Ko B S,Edgar T F.Assessment of achievable PI control performance for linear processes with dead time[C]∥Proceedings of the American Control Conference.Pennsylvania USA:IEEE,1998:1548-1552.
[14]钱积新,赵均,徐祖华.预测控制[M].北京:化学工业出版社,2007:1-3.
[15]田智,吴刚.模块多变量预测控制[C]∥中国控制会议论文集.安徽省黄山市:中国自动化学会控制理论专业委员会,1995:642-648.
[16]Tao Z,Gang W,De-Feng H.Nonlinear model predictive control based on lexicographic multi-objective genetic algorithm[C]∥International Conference on Intelligent and Advanced Systems 2007.[s.l.]:IEEE,2007:61-65.
[17]Kerrigan E,Maciejowshi J.Designing model predictive controllers with prioritised constraints and objectives[C]∥Proc of IEEE Int Symposium on ComputerAided Control System Design.Glasgov:IEEE,2002:33-38.
[18]何德峰,宋秀兰,黄骅.优先级多目标稳定化约束模型预测控制[J].控制与决策,2013,28(12):1831-1836.HE De-feng,SONG Xiu-lan,HUANG Hua.Stabilizing constrained model predictive control with prioritized multi-objectives[J].Control and Decision,2013,28(12):1831-1836.
[19]李少远,席裕庚.模糊动态环境下复杂系统的满意优化控制[J].自动化学报,2002,28(3):408-412.LI Shao-yuan,XI Yu-geng.Satisfactory optimization control of complex systems in fuzzy dynamic environment[J].Acta Automatica Sinica,2002,28(3):408-412.
[20]Muske K R,Finegan C S.Analysis of a class of statistical techniques for estimating the economic benefit from improved process control[C]∥AIChE Symposium Series.New York:American Institute of Chemical Engineers,2002:408-412.
[21]徐玖平,李军.多目标决策的理论与方法[M].北京:清华大学出版社,2005.
[22]席裕庚,谷寒雨.有约束多目标多自由度优化的可行性分析及软约束调整[J].自动化学报,1998.24(6):727-732.XI Yu-geng,GU Han-yu.Feasibility analysis and soft constraints adjustment of CMMO[J].Acta Automatica Sinica,1998.24(6):727-732.
[23]De Oliveira M C,Geromel J C,Bernussou J.Extended H2and H norm characterizations and controller parameterizations for discrete-time systems[J].International Journal of Control,2002,75(9):666-679.
[24]Liu J,Muoz de la Pea D,Christofides P D.Distributed model predictive control of nonlinear process systems[J].AIChE Journal,2009,55(5):1171-1184.