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Point-to-Point Tracking of Integrated Predictive Iterative Learning Control By Using Updating-Reference and CARIMA Model

Lookup NU author(s): Dr Jie ZhangORCiD


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For point-to-point tracking control problem of batch process, a novel design method based on Controlled Auto-regressive Integrated Moving Average (CARIMA) model and updating-reference is proposed in this paper. In the proposed approach, integrated predictive iterative learning control (IPILC) is used for the trajectory tracking control. Comparing with other point-to-point tracking control algorithms, the proposed control scheme performs better in robustness, and reduces the computation load which occurs in those algorithms based on the lifted model for non-Lyapunov-stable systems. Furthermore, updating-reference relaxes the constraints for system outputs and leads to faster convergence than the fixed-reference control algorithms. Simulation results on typical systems show the effectiveness of the proposed algorithm.

Publication metadata

Author(s): Qiu WW, Xiong ZH, Li WZ, Zhang J

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 2016 Chinese Control and Decision Conference (CCDC)

Year of Conference: 2016

Pages: 4993-4997

Online publication date: 08/08/2016

Acceptance date: 02/04/2016

ISSN: 1948-9447

Publisher: IEEE


DOI: 10.1109/CCDC.2016.7531887

Library holdings: Search Newcastle University Library for this item

ISBN: 9781467397155