DOI: 10.3724/SP.J.1219.2013.00706

Information and Control (信息与控制) 2013/42:6 PP.706-713

Strategy and Performance Analysis for Descending Priority of Soft Constraint Tuning in the Steady-State Target Calculation

A weight based soft constraint tuning method is introduced to deal with the infeasibility problem of steady-state target calculation. In order to show the priorities of the constraints in soft constraint tuning, a strategy for descending priority is proposed. The constraints with high priority will be satisfied firstly. Furthermore, a comparison of descending priority strategy with the weight based strategy and the ascending priority strategy is made from the feasible zone's size and computation complexity analysis. The theoretical analysis shows that the advantage of descending priority strategy relative to descending priority strategy and the weight based strategy. Finally, a simulation of Shell's standard heavy oil fractionator is done to verify the validity and advantage of the descending priority strategy.

Key words:soft constraint tuning,descending priority,feasible zone,computation complexity

ReleaseDate:2015-04-15 18:52:40

[1] Lee K H, Tamayo E C, Huang B. Industrial implementation of controller performance analysis technology[J]. Control Engineering Practice, 2010, 18(2): 147-158.

double-layer model predictive control[D]. Hangzhou: Zhejiang University of Technology, 2012.

[2] Jiang H, Shah S L, Huang B. Model analysis and performance analysis of two industrial MPCs[J]. Control Engineering Practice, 2012, 20(3): 219-235.

[3] Rao C V, Rawlings J B. Steady states and constraints in model predictive control[J]. AIChE, 1999, 45(6): 1266-1278.

[4] Xu J, Huang X L, Mu X, et al. Model predictive control based on adaptive hinging hyperplanes model[J]. Journal of Process Control, 2012, 22(10): 1821-1831.

[5] Rolando Z, Hector B. Model predictive control with soft constraints with application to lime kiln control[J]. Computers and Chemical Engineering, 1999, 23(6): 791-806.

[6] Kassmann D E, Badgwell T A, Hawkins R B. Robust steady-state target calculation for model predictive control[J]. AIChE, 2000, 46(5): 1007-1024.

[7] 张惜岭,罗雄麟,王书斌.过程预测控制中约束可行性研究与在线调整[J].化工学报,2012,63(5):1459-1467. Zhang X L, Luo X L, Wang S B. Feasibility analysis and on-line adjustment of constraints in process predictive control[J]. CIESC Journal, 2012, 63(5): 1459-1467.

[8] Lee K H, Huang B, Tamayo E C. Sensitivity analysis for selective constraint and variability tuning in performance assessment of industrial MPC[J]. Control Engineering Practice, 2010, 16(10): 147-158.

[9] Huusom J K, Poulsen N K, Jorgenson S B. Tuning SISO offset-free model predictive control based on ARX models[J]. Journal of Process Control, 2012, 22(10): 1997-2007.

[10] 王宇红,王学剑.基于协方差基准的模型预测控制性能评价与监视[J].信息与控制,2010,39(6):694-699. Wang Y H, Wang X J. Performance assessment and monitoring of model predictive control based on covariance benchmark[J]. Information and Control, 2010, 39(6): 694-699.

[11] 蔡星,谢磊,苏宏业,等.基于串联结构的分布式模型预测控制[J].自动化学报,2013,39(5):510-518. Cai X, Xie L, Shu H Y, et al. Distributed model predictive control based on cascade process[J]. Acta Automatica Sinica, 39(5): 510-518.

[12] Backx T, Bosgra O, Marquardt W. Integration of model predictive control and optimization of processes[C]//Proceedings of ADCHEM. 2000: 249-260.

[13] 阎纲,梁昔明,龙祖强,等.基于最小二乘支持向量机的双模控制[J].信息与控制,2011,40(6):721-727. Yan G, Liang X M, Long Z Q, et al. Double mode control based on least squares support vector machine[J]. Information and Control, 2011, 40(6): 721-727.

[14] Nikandrov A, Swartz C L E. Sensitivity analysis of LP-MPC cascade control systems[J]. Journal of Process Control, 2009, 19(1): 16-24.

[15] 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 Y G, Gu H Y. Feasibility analysis and soft constraint tuning of constrained multi-objective multi degree of freedom optimization[J]. Acta Automatic Sinica, 1998, 24(6): 727-732.

[16] 李海强.双层结构预测控制算法设计与理论分析[D].杭州:浙江工业大学,2012.Li H Q. Research on algorithm design and theory analysis of