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Experimental Validation of Iterative Learning Control for DC/DC Power Converters

Author

Listed:
  • Bingqiang Li

    (School of Automation, Northwestern Polytechnical University, Xi’an 710072, China)

  • Saleem Riaz

    (School of Automation, Northwestern Polytechnical University, Xi’an 710072, China)

  • Yiyun Zhao

    (School of Automation, Northwestern Polytechnical University, Xi’an 710072, China)

Abstract

In order to solve the problem that the parameters of traditional proportional–integral (PI) control are not easy to adjust, an iterative learning control (ILC) technique for a DC/DC power converter is proposed in this paper. Firstly, we have developed a system which is composed of two different states of DC/DC converter in order to obtain its equivalent linear time-varying system, and then the open-loop PD-type ILC law has been used to control it. Secondly, an experimental setup is arranged to verify and compare the simulated results. The experimental results show that, as compared with the traditional PI control, the proposed strategy is easy to implement and optimal with regard to debugging parameters, and it can achieve zero steady-state tracking errors without overshooting. Finally, the experimental results have also proven that our proposed scheme of iterative learning control for a DC/DC power converter is robust as compared to traditional PI control.

Suggested Citation

  • Bingqiang Li & Saleem Riaz & Yiyun Zhao, 2023. "Experimental Validation of Iterative Learning Control for DC/DC Power Converters," Energies, MDPI, vol. 16(18), pages 1-16, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6555-:d:1238121
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    References listed on IDEAS

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    1. Wang, Xin & Luo, Yingbing & Qin, Bin & Guo, Lingzhong, 2022. "Power dynamic allocation strategy for urban rail hybrid energy storage system based on iterative learning control," Energy, Elsevier, vol. 245(C).
    2. Rezk, Hegazy & AL-Oran, Mazen & Gomaa, Mohamed R. & Tolba, Mohamed A. & Fathy, Ahmed & Abdelkareem, Mohammad Ali & Olabi, A.G. & El-Sayed, Abou Hashema M., 2019. "A novel statistical performance evaluation of most modern optimization-based global MPPT techniques for partially shaded PV system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    3. Ning Ma & Huaixian Yin & Kai Wang, 2023. "Prediction of the Remaining Useful Life of Supercapacitors at Different Temperatures Based on Improved Long Short-Term Memory," Energies, MDPI, vol. 16(14), pages 1-14, July.
    4. Saleem Riaz & Hui Lin & Muhammad Waqas & Farkhanda Afzal & Kai Wang & Nasir Saeed, 2021. "An Accelerated Error Convergence Design Criterion and Implementation of Lebesgue-p Norm ILC Control Topology for Linear Position Control Systems," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, November.
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    Cited by:

    1. Selma Ben Attia & Sultan Alzahrani & Saad Alhuwaimel & Salah Salhi & Houssem Eddine Ouerfelli, 2024. "Polynomial Iterative Learning Control (ILC) Tracking Control Design for Uncertain Repetitive Continuous-Time Linear Systems Applied to an Active Suspension of a Car Seat," Mathematics, MDPI, vol. 12(16), pages 1-22, August.

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