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.
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