文献类型：期刊文献

英文题名：Control-Oriented Two-Dimensional Online System Identification for Batch Processes

作者：Gao, Kaihua[1];Lu, Jingyi[3,4];Xu, Zuhua[2];Gao, Furong[1]

机构：[1]Hong Kong Univ Sci & Technol, Dept Chem & Biol Engn, Hong Kong 999077, Peoples R China;[2]Zhejiang Univ, Coll Control Sci & Engn, Natl Ctr Int Res Qual Targeted Proc Optimizat & C, Hangzhou 310027, Peoples R China;[3]East China Univ Sci & Technol, MOE Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[4]Paderborn Univ, Dept Elect Engn EIM E, D-33098 Paderborn, Germany

年份：2021

卷号：60

期号：20

起止页码：7656

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20212210415187);WOS:【SCI-EXPANDED(收录号:WOS:000657198400011)】；

基金：This work is supported by Hong Kong Research Council under project no. 16208520 and Chinese Ministry Project under project no. 2019YFB1704900.

语种：英文

外文关键词：Batch data processing - Predictive control systems - Robust control - System theory

摘要：Robust model predictive control (MPC) is widely applied to batch processes to handle its intrinsic plant-model mismatch by considering the worst-case scenario and thus leads to conservative performance. Identification methods that make use of system repetitiveness have been invented and combined with robust MPC to reduce conservativeness. In this paper, we show that in addition to the repetitiveness, time-wise continuity in system dynamics can also help to improve model accuracy and accelerate the convergence rate. Specifically, we propose a two-dimensional online identification method in the framework of set membership identification. The method is capable of (1) employing time-wise correlation and cycle-wise repetitiveness in system dynamics; (2) identifying both of the nominal value and the corresponding uncertainty set of the unknown parameters, thus being amenable to robust MPC schemes; and (3) guaranteeing convergence to the true parameters under mild conditions. Numerical examples are provided to illustrate its fast convergence and superiority in improving prediction accuracy and control performance.