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Adaptive Neural Backstepping Terminal Sliding Mode Control of a DC-DC Buck Converter.

Xiaoyu Gong1,2, Juntao Fei1,2

  • 1Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, College of Information Science and Engineering, Hohai University, Changzhou 213022, China.

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This study introduces an adaptive backstepping terminal sliding mode control (ABTSMC) for DC-DC converters, using a neural network to handle system uncertainties for improved performance.

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DC-DC buck converterbackstepping controldouble hidden layer recurrent neural networkterminal sliding mode control

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Area of Science:

  • Electrical Engineering
  • Control Systems
  • Power Electronics

Background:

  • DC-DC converters are crucial for power management.
  • System uncertainties and disturbances affect converter performance.
  • Existing control methods may have limitations in convergence speed and steady-state accuracy.

Purpose of the Study:

  • To propose a novel adaptive backstepping terminal sliding mode control (ABTSMC) method for DC-DC buck converters.
  • To enhance converter performance by addressing system uncertainties.
  • To achieve finite-time convergence of tracking errors.

Main Methods:

  • Utilizing a double hidden layer recurrent neural network (DHLRNN) for approximating system uncertainties.
  • Implementing a backstepping control framework.
  • Integrating terminal sliding mode control (TSMC) for finite-time convergence.
  • Experimental validation on a DC-DC buck converter prototype.

Main Results:

  • The proposed DHLRNN effectively approximates and compensates for system uncertainties.
  • The ABTSMC method ensures finite-time convergence of tracking errors.
  • Experimental results show superior steady-state performance compared to other methods.
  • The control strategy demonstrates a faster transient response.

Conclusions:

  • The proposed adaptive backstepping terminal sliding mode control (ABTSMC) method effectively improves DC-DC buck converter performance.
  • The integration of DHLRNN and TSMC offers a robust solution for handling system uncertainties and achieving fast, accurate control.
  • The method is validated experimentally, demonstrating its practical applicability and advantages.