Wavelet neural network fractional-order sliding mode control of DC-DC buck converter

In this article, a fractional-order sliding mode control with an adaptive wavelet neural network is proposed for accurate tracking of the output voltage of DC-DC buck converter. The fractional-order sliding mode control introduces a fractional operator into sliding mode surface, which ensures rapid convergence of tracking error and reduces chattering. To compensate the unknown nonlinear dynamics and parameter perturbations in the system, the adaptive wavelet neural network is designed to estimate unknown nonlinear functions online. Adaptive wavelet neural network performs multi-scale analysis and feature fusion on input signals through wavelet functions to estimate nonlinear functions, and adaptively updates its network parameters via Lyapunov stability theory and the gradient descent method. Hardware experiments have shown that the proposed control method has a fast dynamic response, and strong disturbance rejection ability, validating the effectiveness and superiority of the proposed control strategy.