基于自适应迭代学习控制的MPC系统经济性能设计

王振雷; 刘学彦; 王昕

doi:10.16511/j.cnki.qhdxxb.2016.24.024

PDF(2636 KB)

清华大学学报（自然科学版） ›› 2016, Vol. 56 ›› Issue (9) : 1016-1024. DOI: 10.16511/j.cnki.qhdxxb.2016.24.024

化学与化学工程

基于自适应迭代学习控制的MPC系统经济性能设计

王振雷¹, 刘学彦¹, 王昕²

作者信息 +

Economic performance design based on adaptive iterative learning control of MPC systems

WANG Zhenlei¹, LIU Xueyan¹, WANG Xin²

Author information +

文章历史 +

摘要

针对模型预测控制（model predictive control，MPC）系统经济性能设计问题，结合自适应迭代学习控制的设计思想，提出了一种自适应步长迭代学习控制（adaptive step iterative learning control，ASILC）策略。该策略将系统变量方差与控制器参数之间的关系近似成离散的线性区间组合，并借助上一步迭代的过程信息，自适应地更新迭代步长，逐步使系统的经济性能达到最优。将该方法应用于乙烯裂解炉控制系统中，仿真结果表明：与迭代学习控制方法相比，ASILC能更快地收敛到最优工作点附近，得到最优经济性能下的控制器参数λ，经过7次优化迭代后经济性能目标值提高了28.92%。

Abstract

An adaptive step iterative learning control (ASILC) strategy was developed for model predictive control (MPC) system economic performance design. The strategy treats the functional relationship between the variable variances and the controller parameters as a combination of discrete linear intervals and uses process information in the last iteration to adaptively update the iteration step. This optimizes the economic performance step by step. The method is used to design an ethylene cracking furnace control system. Simulations show that ASILC converges to the optimal operating point faster than iterative learning control (ILC) and obtains the controller parameter λ for the optimal economic performance. After seven optimizations and iterations, the economic performance target was improved 28.92%.

导出引用

王振雷, 刘学彦, 王昕. 基于自适应迭代学习控制的MPC系统经济性能设计[J]. 清华大学学报（自然科学版）. 2016, 56(9): 1016-1024 https://doi.org/10.16511/j.cnki.qhdxxb.2016.24.024

WANG Zhenlei, LIU Xueyan, WANG Xin. Economic performance design based on adaptive iterative learning control of MPC systems[J]. Journal of Tsinghua University(Science and Technology). 2016, 56(9): 1016-1024 https://doi.org/10.16511/j.cnki.qhdxxb.2016.24.024

中图分类号： TP273

参考文献

[1] Harris T J. Assessment of control loop performance [J]. Canadian Journal of Chemical Engineering, 1989, 67(5): 856-861.
[2] HUANG Biao, Shah S L, Kwok E K. Good, bad or optimal? Performance assessment of multivariable processes [J]. Automatic, 1997, 33 (6): 1175-1183.
[3] HUANG Biao, Shah S L. Performance Assessment of Control Loops: Theory and Applications [M]. London, UK: Springer, 1999.
[4] HUANG Biao. A pragmatic approach towards assessment of control loop performance [J]. International Journal of Adaptive Control and Signal Processing, 2003, 17(729): 589-608.
[5] Grimble M J. Controller performance benchmarking and tuning using generalized minimum variance control [J]. Automatic, 2002, 38(12): 2111-2119.
[6] Bauer M, Craig I K. Economic assessment of advanced process control—A survey and framework [J]. Journal of Process Control, 2008, 18(1): 2-18.
[7] Martin G D, Turpin L E, Cline R P. Estimating control function benefits [J]. Hydrocarbon Processing, 1991, 70(6): 68-73.
[8] Latour P R. Quantify quality control’s intangible benefits [J]. Hydrocarbon Processing, 1992, 71(5): 61-65.
[9] Bao J, Mclellan P, Forbes J. Towards a systematic approach for control system benefits analysis [C]//Proceedings of the Control Systems, Preprints, Conference. Norcross, USA: Technical Association of the Pulp and Paper Industry Press, 2000: 334-340.
[10] 赵超. 过程控制系统经济性能评估算法的研究 [D]. 杭州: 浙江大学, 2009. ZHAO Chao. Economic Performance Assessment Algorithm Research of Process Control System [D]. Hangzhou: Zhejiang University, 2009. (in Chinese)
[11] Sorensen R C, Cutler C R. LP integrates economics into dynamic matrix control [J]. Hydrocarbon Processing, 1998, 77(9): 57-65.
[12] Krishman A, Kosanovich K, De Witt M, et al. Robust model predictive control of an industrial solid phase polymerizer [C]//Proceedings of the American control Conference. Philadelphia, USA: American Automatic Control Council, 1998.
[13] XU Fangwei, HUANG Biao, Akande S. Performance Assessment of model predictive control for variability and constraint tuning [J]. Industrial and Engineering Chemistry Research, 2007, 46(4): 1208-1219.
[14] ZHAO Chao, ZHAO Yu, SU Hongye, et al. Economic performance assessment of advanced process control with LQG benchmarking [J]. Journal of Process Control, 2009, 19(4): 557-569.
[15] Moore K L. Iterative Learning Control for Deterministic Systems [M]. London, UK: Springer-Verlag, 1993.
[16] Ahn H S, CHEN Yangquan, Moore K L. Iterative learning control: Brief survey and categorization [J] IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2007, 37(6): 1099-1121.
[17] CAI Xing, SUN Pei, CHEN Junhui, et al. ILC strategy for progress improvement of economic performance in industrial model predictive control systems [J]. Journal of Process Control, 2014, 24(12): 373-389.
[18] 杜志国, 曾清泉, 陈硕. 管式裂解炉辐射区裂解管结焦模型进展 [J]. 石油化工, 2003, 32(4): 348-352. DU Zhiguo, ZENG Qingquan, CHEN Shuo. Progress in coking model of tube in radiation section of cracking furnace [J]. Petrochemical Technology, 2003, 32(4): 348-352. (in Chinese)

PDF(2636 KB)

Accesses

Citation

Detail

段落导航

收稿日期	出版日期
2015-08-25	2016-09-15
发布日期
2016-09-15

选择文件类型/文献管理软件名称

选择包含的内容

摘要

Abstract

关键词

Key words

引用本文

{{custom_sec.title}}

{{custom_sec.title}}

参考文献

访问统计

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

摘要

Abstract

关键词

Key words

引用本文

{{custom_sec.title}}

{{custom_sec.title}}

参考文献

访问统计