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Lookup NU author(s): Dr Zhihua Xiong, Dr Jie ZhangORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© 2019 Elsevier Ltd. A novel control technique is proposed by combining iterative learning control (ILC) and model predictive control (MPC) with updating-reference trajectory for point-to-point tracking problem of batch process. In this paper, a batch-to-batch updating-reference trajectory, which passes through the desired points, is firstly designed as the tracking trajectory within a batch. The updating control law consists of P-type ILC part and MPC part, in which P-type ILC part can improve the performance by learning from previous executions and MPC part is used to suppress the model perturbations and external disturbances. Convergence properties of the integrated predictive iterative learning control (IPILC) are analyzed theoretically, and the sufficient convergence conditions of output tracking error are also derived for a class of linear systems. Comparing with other point-to-point tracking control algorithms, the proposed algorithm can perform better in robustness. Furthermore, updating-reference relaxes the constraints for system outputs, and it may lead to faster convergence and more extensive range of application than those of fixed-reference control algorithms. Simulation results on typical systems show the effectiveness of the proposed algorithm.
Author(s): Qiu W, Xiong Z, Zhang J, Hong Y, Li W
Publication type: Article
Publication status: Published
Journal: Journal of Process Control
Year: 2020
Volume: 85
Pages: 41-51
Print publication date: 01/01/2020
Online publication date: 13/11/2019
Acceptance date: 05/11/2019
Date deposited: 12/11/2019
ISSN (print): 0959-1524
ISSN (electronic): 1873-2771
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.jprocont.2019.11.003
DOI: 10.1016/j.jprocont.2019.11.003
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