文献类型：期刊文献

英文题名：Optimal Iterative Learning Control for Batch Processes in the Presence of Time-Varying Dynamics

作者：Lu, Jingyi[1,2];Cao, Zhixing[1];Hu, Qinran[3];Xu, Zuhua[4];Du, Wenli[1];Gao, Furong[5]

机构：[1]East China Univ Sci & Technol, MOE Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Paderborn Univ, Dept Elect Engn & Informat Technol, D-33098 Paderborn, Germany;[3]Southeast Univ, Sch Elect Engn, Nanjing 210096, Peoples R China;[4]Zhejiang Univ, Coll Control Sci & Engn, Natl Ctr Int Res Qual Targeted Proc Optimizat & C, Hangzhou 310027, Peoples R China;[5]Hong Kong Univ Sci & Technol, Dept Chem & Biol Engn, Hong Kong, Peoples R China

年份：2022

卷号：52

期号：1

起止页码：680

外文期刊名：IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS

收录：;EI(收录号：20204909564035);WOS:【SCI-EXPANDED(收录号:WOS:000731147700066)】；

基金：The work of Zhixing Cao and Wenli Du was supported by the National Natural Science Foundation of China under Grant 61988101 and Grant 62073137. The work of Zuhua Xu was supported by the National Key Research and Development Program of China under Grant 2017YFB0603703. The work of Furong Gao was supported by the Hong Kong Research Grant Council under Grant 16207717. This article was recommended by Associate Editor T. I. Strasser.

语种：英文

外文关键词：Uncertainty; Computational modeling; Optimization; Convergence; Batch production systems; Time-varying systems; Upper bound; Batch process; minimax optimization; optimal iterative learning control (OILC); robust monotonic convergence; time-varying uncertainty

摘要：Optimal iterative learning control (OILC) has been recognized as an excellent model-based means for regulating batch process with abundant successful applications reported in the past decades but also received considerable criticisms for its poor robustness against model mismatch that is common for many industrial situations. Despite numerous attempts to address the issue, many of them are still not able to yield satisfactory control performance particularly in the presence of a possible combination of time-varying uncertainties and conservatively designed controllers, which may compromise the learning mechanism, hence rendering the robustness issue of OILC far from well explored. This article intends to investigate the aforementioned issue by proposing a new OILC method resting upon the minimization of a dynamic upper bound on tracking error which is distilled from better exploitation of the time variation of uncertainties. We also show that the problem can be formulated in the framework of convex-concave game that can be efficiently solved by a subgradient method with an excellent balance of optimality and computation time. Such a formulation enables us to gain: 1) guaranteed monotonic convergence on tracking error; 2) remarkably reduced conservatism on controller synthesis; and 3) controllable computation complexity. It is further shown that the proposed method is capable of handling nonlinearity, for example Volterra system, a classic representation of nonlinear process. The efficacy of the method is verified by numerical experiments on a continuous stirred tank reactor model.