Design and Implementation of DC-DC Buck Converter based on Deep Neural Network Sliding Mode Control

TL;DR

This paper introduces a DNN-based sliding mode control strategy for DC-DC buck converters that dynamically adjusts parameters to improve convergence speed and jitter suppression, validated through simulations and hardware tests.

Contribution

It proposes a novel DNN-based sliding mode control method that enhances system stability and performance over traditional controllers for power supply regulation.

Findings

Faster convergence speed compared to conventional controllers.

Improved jitter suppression and robustness.

Validated through simulation and hardware experiments.

Abstract

In order to address the challenge of traditional sliding mode controllers struggling to balance between suppressing system jitter and accelerating convergence speed, a deep neural network (DNN)-based sliding mode control strategy is proposed in this paper. The strategy achieves dynamic adjustment of parameters by modelling and learning the system through deep neural networks, which suppresses the system jitter while ensuring the convergence speed of the system. To demonstrate the stability of the system, a Lyapunov function is designed to prove the stability of the mathematical model of the DNN-based sliding mode control strategy for DC-DC buck switching power supply. We adopt a double closed-loop control mode to combine the sliding mode control of the voltage inner loop with the PI control of the current outer loop. Simultaneously, The DNN performance is evaluated through simulation and hardware experiments and compared with conventional control methods. The results demonstrate that the sliding mode controller based on the DNN exhibits faster system convergence speed, enhanced jitter suppression capability, and greater robustness.