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State Switched Discrete-Time Model and Digital Predictive Voltage Programmed Control for Buck Converters

Author

Listed:
  • Wei Wang

    (Shanghai Institute of Satellite Engineering, Shanghai 200240, China)

  • Gaoshuai Shen

    (School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Run Min

    (School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Qiaoling Tong

    (School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Qiao Zhang

    (School of Automation, Wuhan University of Technology, Wuhan 430074, China)

  • Zhenglin Liu

    (School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Switched mode power converters are nonlinear systems, and it is a constant challenge to improve their modeling accuracy and control performance. In this paper, a State Switched Discrete-time Model (SSDM) is proposed, which achieves a higher accuracy at a high frequency than that of conventional state averaged models. Instead of averaging the converter states for approximation, the states within each switching cycle are considered in the modeling. Based on total differential equations of switching-ON and switching-OFF durations, the inductor current and output voltage within a cycle are accurately calculated, which derives the SSDM. Furthermore, a Digital Predictive Voltage Programmed (DPVP) control strategy is derived through the SSDM. Through voltage prediction, a suitable duty ratio is calculated that regulates the output voltage to its reference value in the minimum switching cycles. In this way, the converter achieves a very fast load/line transient response and reference tracking speed, and it exhibits a high stability under deviated inductance. Finally, the accuracy of SSDM and the system stability are proved by frequency response analyses and experiments.

Suggested Citation

  • Wei Wang & Gaoshuai Shen & Run Min & Qiaoling Tong & Qiao Zhang & Zhenglin Liu, 2020. "State Switched Discrete-Time Model and Digital Predictive Voltage Programmed Control for Buck Converters," Energies, MDPI, vol. 13(13), pages 1-21, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3451-:d:379985
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    References listed on IDEAS

    as
    1. Run Min & Dian Lyu & Shuai Cheng & Yingshui Sun & Linkai Li, 2019. "Linearized Discrete Charge Balance Control with Simplified Algorithm for DCM Buck Converter," Energies, MDPI, vol. 12(16), pages 1-17, August.
    2. Stefania Cuoghi & Riccardo Mandrioli & Lorenzo Ntogramatzidis & Grandi Gabriele, 2020. "Multileg Interleaved Buck Converter for EV Charging: Discrete-Time Model and Direct Control Design," Energies, MDPI, vol. 13(2), pages 1-18, January.
    3. Enric Vidal-Idiarte & Carlos Restrepo & Abdelali El Aroudi & Javier Calvente & Roberto Giral, 2019. "Digital Control of a Buck Converter Based on Input-Output Linearization. An Interpretation Using Discrete-Time Sliding Control Theory," Energies, MDPI, vol. 12(14), pages 1-17, July.
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    Cited by:

    1. Liangliang Lu & Gaoshuai Shen & Haoran Xu & Qiaoling Tong & Run Min & Qiao Zhang & Jun Yuan & Ningyu Liu, 2022. "Triple-Mode Average Current Control with Valley Current Shaping for DCM/CRM/CCM Boost PFC Converter," Energies, MDPI, vol. 15(19), pages 1-23, October.
    2. Jaime A. Rohten & David N. Dewar & Pericle Zanchetta & Andrea Formentini & Javier A. Muñoz & Carlos R. Baier & José J. Silva, 2021. "Multivariable Deadbeat Control of Power Electronics Converters with Fast Dynamic Response and Fixed Switching Frequency," Energies, MDPI, vol. 14(2), pages 1-16, January.

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