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Asian Simulation Conference

SCS Autumn Simulation Multi-Conference

AsiaSim 2016, SCS AutumnSim 2016: Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems pp 127–135Cite as

An Integrated Model Predictive Iterative Learning Control Strategy for Batch Processes

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 643))

Abstract

A novel integrated model predictive iterative learning control (MPILC) strategy is proposed in this paper. It systematically integrates batch-axis information and time-axis information into one uniform frame, namely the iterative learning controller (ILC) in the domain of batch-axis, while a model predictive controller (MPC) with time-varying prediction horizon in the domain of time-axis. As a result, the operation policy of batch process can be regulated during one batch, which leads to superior tracking performance and better robustness against disturbance and uncertainty. The convergence and tracking performance of the proposed learning control system are firstly given rigorous description and proof. Lastly, the effectiveness of the proposed method is verified by examples.

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References

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Author information

Authors and Affiliations

  1. Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China

    Chao Han & Li Jia

Authors
  1. Chao Han
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  2. Li Jia
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Corresponding author

Correspondence to Li Jia .

Editor information

Editors and Affiliations

  1. Beihang University, Beijing, China

    Lin Zhang

  2. Beihang University, Beijing, China

    Xiao Song

  3. Beihang University, Beijing, China

    Yunjie Wu

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© 2016 Springer Science+Business Media Singapore

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Han, C., Jia, L. (2016). An Integrated Model Predictive Iterative Learning Control Strategy for Batch Processes. In: Zhang, L., Song, X., Wu, Y. (eds) Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. AsiaSim SCS AutumnSim 2016 2016. Communications in Computer and Information Science, vol 643. Springer, Singapore. https://doi.org/10.1007/978-981-10-2663-8_14

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  • DOI: https://doi.org/10.1007/978-981-10-2663-8_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2662-1

  • Online ISBN: 978-981-10-2663-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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