Economic performance design based on adaptive iterative learning control of MPC systems
WANG Zhenlei1, LIU Xueyan1, WANG Xin2
1. Key Laboratory of Advanced Control and Optimization for Chemical Processes of the Ministry of Education, East China University of Science and Technology, Shanghai 200237, China;
2. Center of Electrical and Electronic Technology, Shanghai Jiao Tong University, Shanghai 200240, China
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%.
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.
WANG Zhenlei, LIU Xueyan, WANG Xin. Economic performance design based on adaptive iterative learning control of MPC systems. Journal of Tsinghua University(Science and Technology), 2016, 56(9): 1016-1024.
[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)
2016, Vol. 56 
