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This paper proposes a neural network explicit model predictive controller for buck converter model switch between continuous conduction mode (CCM) and discontinuous conduction mode (DCM). The control strategy linearizes the model at different working points and designs explicit model predictive control (EMPC) one by one to obtain the control law at each working point. The control law is trained as a training sample of the neural network. Finally, the trained neural network parameters are extracted to field programmable gate array (FPGA). The experimental results show that the output voltage accuracy of the converter is improved during model switch between CCM and DCM, having small overshoot and short recovery time.

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A Neural Network Explicit Model Predictive Controller for Buck Converter Model Switch between CCM and DCM

  • 会议论文

  • 发表日期：

    2026年

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  • Limei Shi,
  • Zhiyang Tang,
  • Peng Luo,
  • Lei Guo,
  • Huadong Lai

摘要

This paper proposes a neural network explicit model predictive controller for buck converter model switch between continuous conduction mode (CCM) and discontinuous conduction mode (DCM). The control strategy linearizes the model at different working points and designs explicit model predictive control (EMPC) one by one to obtain the control law at each working point. The control law is trained as a training sample of the neural network. Finally, the trained neural network parameters are extracted to field programmable gate array (FPGA). The experimental results show that the output voltage accuracy of the converter is improved during model switch between CCM and DCM, having small overshoot and short recovery time.