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Industrial feedforward control technology: a review

Author

Listed:
  • Lu Liu

    (Northwestern Polytechnical University)

  • Siyuan Tian

    (Lam Research Corporation)

  • Dingyu Xue

    (Northeastern University)

  • Tao Zhang

    (Lam Research Corporation)

  • YangQuan Chen

    (University of California)

Abstract

In the control field, most of the research papers focus on feedback control, but few of them have discussed about feedforward control. Therefore, a review of the most commonly used feedforward control algorithms in industrial processes is necessary to be carried out. In this paper, in order to benefit researchers and engineers with different academic backgrounds, two most representative kinds of feedforward controller design algorithms and some other typical industrial feedforward control benchmarks are presented together with their characteristics, application domains and informative comments for selection. Moreover, some frequently concerned problems of feedforward control are also discussed. An industrial data driven example is presented to show how feedforward controller works to improve system performance and achieve the maximum economic profits.

Suggested Citation

  • Lu Liu & Siyuan Tian & Dingyu Xue & Tao Zhang & YangQuan Chen, 2019. "Industrial feedforward control technology: a review," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2819-2833, December.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:8:d:10.1007_s10845-018-1399-6
    DOI: 10.1007/s10845-018-1399-6
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    References listed on IDEAS

    as
    1. Karer, Gorazd & Mušič, Gašper & Škrjanc, Igor & Zupančič, Borut, 2011. "Feedforward control of a class of hybrid systems using an inverse model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(3), pages 414-427.
    2. Vladimir Stojanovic & Novak Nedic, 2016. "A Nature Inspired Parameter Tuning Approach to Cascade Control for Hydraulically Driven Parallel Robot Platform," Journal of Optimization Theory and Applications, Springer, vol. 168(1), pages 332-347, January.
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    Citations

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    Cited by:

    1. Phong B. Dao, 2021. "Learning Feedforward Control Using Multiagent Control Approach for Motion Control Systems," Energies, MDPI, vol. 14(2), pages 1-17, January.
    2. Sangho Lee & Youngdoo Son, 2021. "Motor Load Balancing with Roll Force Prediction for a Cold-Rolling Setup with Neural Networks," Mathematics, MDPI, vol. 9(12), pages 1-21, June.
    3. Gil, Juan D. & Topa, A. & Álvarez, J.D. & Torres, J.L. & Pérez, M., 2022. "A review from design to control of solar systems for supplying heat in industrial process applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    4. Wesley Beccaro & Carlos A. S. Ramos & Silvio X. Duarte, 2023. "Optimizing semiconductor processing open tube furnace performance: comparative analysis of PI and Mamdani fuzzy-PI controllers," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3015-3024, October.
    5. Zhicheng Xu & Vignesh Selvaraj & Sangkee Min, 2024. "State identification of a 5-axis ultra-precision CNC machine tool using energy consumption data assisted by multi-output densely connected 1D-CNN model," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 147-160, January.
    6. Waheed Ur Rehman & Xinhua Wang & Yiqi Cheng & Yingchun Chen & Hasan Shahzad & Hui Chai & Kamil Abbas & Zia Ullah & Marya Kanwal, 2021. "Model-Based Design Approach to Improve Performance Characteristics of Hydrostatic Bearing Using Multivariable Optimization," Mathematics, MDPI, vol. 9(4), pages 1-15, February.

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