Just-in-time learning based integrated MPC-ILC control for batch processes  

作　　者：Li Jia Wendan Tan 

机构地区：[1]Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University

出　　处：《Chinese Journal of Chemical Engineering》2018年第8期1713-1720,共8页中国化学工程学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China(61374044);Shanghai Science Technology Commission(15510722100,16111106300);Shanghai Municipal Education Commission(14ZZ088)

摘　　要：Considering the two-dimension(2 D) characteristic and the unknown optimal trajectory problem of the batch processes, an integrated model predictive control-iterative learning control(MPC-ILC) for batch processes is proposed in this paper. Firstly, the batch-axis information and time-axis information are combined into one quadratic performance index. It implies the integration of ILC and MPC algorithm idea, which leads to superior tracking performance and better robustness against disturbance and uncertainty. To address the problem of the unknown optimal trajectory, both time-varying prediction horizon and end product quality control are employed. Moreover, an integrated 2 D just-in-time learning(JITL) model is used to improve the predictive accuracy. Furthermore, rigorous description and proof are presented to prove the convergence and tracking performance of the proposed MPC-ILC strategy. The simulation results show the effectiveness of the proposed method.Considering the two-dimension（2 D） characteristic and the unknown optimal trajectory problem of the batch processes, an integrated model predictive control-iterative learning control（MPC-ILC） for batch processes is proposed in this paper. Firstly, the batch-axis information and time-axis information are combined into one quadratic performance index. It implies the integration of ILC and MPC algorithm idea, which leads to superior tracking performance and better robustness against disturbance and uncertainty. To address the problem of the unknown optimal trajectory, both time-varying prediction horizon and end product quality control are employed. Moreover, an integrated 2 D just-in-time learning（JITL） model is used to improve the predictive accuracy. Furthermore, rigorous description and proof are presented to prove the convergence and tracking performance of the proposed MPC-ILC strategy. The simulation results show the effectiveness of the proposed method.

关 键 词：Model predictive control Batch process Just-in-time learning （JITL） model 

分 类 号：O232[理学—运筹学与控制论]