模型不确定条件下预测控制经济性能评估的研究
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摘要
为了解决在模型不确定条件下的预测控制系统经济性能评估分析的问题,本文通过基于二次锥规划的鲁棒线性规划的方法来描述模型不确定性对控制系统经济性能评估造成的影响,并采用约束调整与方差调整的策略来改善控制系统的经济性能.Shell公司提供的重油分馏塔典型案例实验证明该方法的有效性.
In order to solve the problem in evaluating economic performance for predictive control systems in the case of model uncertainty appropriately a robust linear programme method is proposed based on a second-order cone programme to describe the affect on economic performance assessment due to the model uncertainty.Then some constraint tuning and variability tun-ing are done to improve the economic performance.Simulation results of a typical control problem of a heavy oil fractionator proposed by Royal Dutch/Shell Group showed the effectivity of the proposed algorithm.
In order to solve the problem in evaluating economic performance for predictive control systems in the case of model uncertainty appropriately a robust linear programme method is proposed based on a second-order cone programme to describe the affect on economic performance assessment due to the model uncertainty.Then some constraint tuning and variability tun-ing are done to improve the economic performance.Simulation results of a typical control problem of a heavy oil fractionator proposed by Royal Dutch/Shell Group showed the effectivity of the proposed algorithm.
引文
1 Zhao Chao.Research on Algorithm of Economical Perfor-mance Assessment in Process Control System[Ph.D.disser-tation],Zhejiang University,China,2009(赵超.过程控制系统经济性能评估算法的研究[博士学位论文],浙江大学,中国,2009)
2 Martin G D.Understand control benefits estimates.Hydro-carbon Processing,2004,83(10):43-46
3 Martin G D,Turpin L E,Cline R P.Estimating control func-tion benefits.Hydrocarbon Processing,1991,70(6):68-73
4 Xu Q L,Zhao C,Zhang D F,An A M,Zhang C.Data-drivenLQG benchmaking for economic performance assessment ofadvanced process control systems.In:Proceedings of the2011 American Control Conference.San Francisco:AACC,2011.5085-5090
5 Porfirio C R,Odloak D.Optimizing model predictive con-trol of an industrial distillation column.Control EngineeringPractice,2011,19(10):1137-1146
6 Zhou Y,Forbes J F.Determining controller benefits viaprobabilistic optimization.International Journal of AdaptiveControl and Signal Processing,2003,17(7-9):553-568
7 Agarwal N,Huang B,Tarnayo E C.Assessing model pre-diction control(MPC)performance.2.Bayesian approachfor constraint tuning.Industrial and Engineering ChemistryResearch,2007,46(24):8112-8119
8 Agarwal N,Huang B,Tamayo E C.Assessing model predic-tion control(MPC)performance.1.Probabilistic approachfor constraint analysis.Industrial and Engineering Chem-istry Research,2007,46(24):8101-8111
9 Akande S,Huang B,Lee K H.MPC constraint analysis-bayesian approach via a continuous-valued profit function.Industrial and Engineering Chemistry Research,2009,48(8):3944-3954
10 Lee K H,Huang B,Tamayo E C.Sensitivity analysis for se-lective constraint and variability tuning in performance as-sessment of industrial MPC.Control Engineering Practice,2008,16(10):1195-1215
11 Boyd S,Vandenberghe L.Convex Optimization.Cambridge:Cambridge University Press,2004
12 Kassmann D E,Badgwell T A,Hawkins R B.Robust steady-state target calculation for model predictive control.AicheJournal,2000,46(5):1007-1024
13 Xu F W,Huang B,Akande S.Performance assessment ofmodel pedictive control for variability and constraint tuning.Industrial and Engineering Chemistry Research,2007,46(4):1208-1219
2 Martin G D.Understand control benefits estimates.Hydro-carbon Processing,2004,83(10):43-46
3 Martin G D,Turpin L E,Cline R P.Estimating control func-tion benefits.Hydrocarbon Processing,1991,70(6):68-73
4 Xu Q L,Zhao C,Zhang D F,An A M,Zhang C.Data-drivenLQG benchmaking for economic performance assessment ofadvanced process control systems.In:Proceedings of the2011 American Control Conference.San Francisco:AACC,2011.5085-5090
5 Porfirio C R,Odloak D.Optimizing model predictive con-trol of an industrial distillation column.Control EngineeringPractice,2011,19(10):1137-1146
6 Zhou Y,Forbes J F.Determining controller benefits viaprobabilistic optimization.International Journal of AdaptiveControl and Signal Processing,2003,17(7-9):553-568
7 Agarwal N,Huang B,Tarnayo E C.Assessing model pre-diction control(MPC)performance.2.Bayesian approachfor constraint tuning.Industrial and Engineering ChemistryResearch,2007,46(24):8112-8119
8 Agarwal N,Huang B,Tamayo E C.Assessing model predic-tion control(MPC)performance.1.Probabilistic approachfor constraint analysis.Industrial and Engineering Chem-istry Research,2007,46(24):8101-8111
9 Akande S,Huang B,Lee K H.MPC constraint analysis-bayesian approach via a continuous-valued profit function.Industrial and Engineering Chemistry Research,2009,48(8):3944-3954
10 Lee K H,Huang B,Tamayo E C.Sensitivity analysis for se-lective constraint and variability tuning in performance as-sessment of industrial MPC.Control Engineering Practice,2008,16(10):1195-1215
11 Boyd S,Vandenberghe L.Convex Optimization.Cambridge:Cambridge University Press,2004
12 Kassmann D E,Badgwell T A,Hawkins R B.Robust steady-state target calculation for model predictive control.AicheJournal,2000,46(5):1007-1024
13 Xu F W,Huang B,Akande S.Performance assessment ofmodel pedictive control for variability and constraint tuning.Industrial and Engineering Chemistry Research,2007,46(4):1208-1219