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Discrete Sliding Mode Control Strategy for Start-Up and Steady-State of Boost Converter

Author

Listed:
  • Tao Yang

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Yong Liao

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

Abstract

Since the zero initial conditions of the boost converter are far from the target equilibrium point, the overshoot of the input current and the output voltage will cause energy loss during the start-up process when the converter adopts the commonly used small-signal model design control method. This paper presents a sliding mode control strategy that combines two switching surfaces. One switching surface based on the large-signal model is employed for the start-up to minimize inrush current and voltage overshoot. The stability of this strategy is verified by Lyapunov theory and simulation. Once the converter reaches the steady-state, the other switching surface with PI compensation of voltage error is employed to improve the robustness. The latter switching surface, which is adopted to regulate the voltage, can not only suppress the perturbation of input voltage and load, but also achieve a better dynamic process and a zero steady-state error. Furthermore, the discrete sliding mode controller is implemented by digital signal processor (DSP). Finally, the results of simulation, experiment and theoretical analysis are consistent.

Suggested Citation

  • Tao Yang & Yong Liao, 2019. "Discrete Sliding Mode Control Strategy for Start-Up and Steady-State of Boost Converter," Energies, MDPI, vol. 12(15), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:2990-:d:254396
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    References listed on IDEAS

    as
    1. Robert Baždarić & Danjel Vončina & Igor Škrjanc, 2018. "Comparison of Novel Approaches to the Predictive Control of a DC-DC Boost Converter, Based on Heuristics," Energies, MDPI, vol. 11(12), pages 1-16, November.
    2. El Manaa Barhoumi & Ikram Ben Belgacem & Abla Khiareddine & Manaf Zghaibeh & Iskander Tlili, 2018. "A Neural Network-Based Four Phases Interleaved Boost Converter for Fuel Cell System Applications," Energies, MDPI, vol. 11(12), pages 1-18, December.
    3. Miran Rodič & Miro Milanovič & Mitja Truntič & Benjamin Ošlaj, 2018. "Switched-Capacitor Boost Converter for Low Power Energy Harvesting Applications," Energies, MDPI, vol. 11(11), pages 1-29, November.
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    Cited by:

    1. Allan G. S. Sánchez & Francisco J. Pérez-Pinal & Martín A. Rodríguez-Licea & Cornelio Posadas-Castillo, 2021. "Non-Integer Order Approximation of a PID-Type Controller for Boost Converters," Energies, MDPI, vol. 14(11), pages 1-18, May.
    2. Mario Villegas-Ruvalcaba & Kelly Joel Gurubel-Tun & Alberto Coronado-Mendoza, 2021. "Robust Inverse Optimal Control for a Boost Converter," Energies, MDPI, vol. 14(9), pages 1-17, April.
    3. Rongchao Niu & Hongyu Zhang & Jian Song, 2023. "Model Predictive Control of DC–DC Boost Converter Based on Generalized Proportional Integral Observer," Energies, MDPI, vol. 16(3), pages 1-16, January.

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