Dynamic Terminal Sliding-Mode Control for Single-Phase Active Power Filter Using New Feedback Recurrent Neural Network
Juntao Fei, Yun Chen
Abstract
In this article, an adaptive dynamic terminal sliding-mode controller using a double hidden layer recurrent neural network (DHL-RNN) structure for a single-phase active power filter (APF) is proposed to improve harmonic compensation performance. First, a method combining dynamic sliding mode and terminal sliding mode is proposed to solve the chattering phenomenon in traditional sliding-mode control. Then, since the nonlinear dynamics of APF is difficult to obtain accurately, the DHL-RNN is used to approximate the proposed dynamic terminal sliding-mode controller. Meanwhile, an integral robust switching term is added to eliminate the approximation error of the neural network. Simulation and experimental results proved that the proposed controller has better compensation performance and tracking effect compared with a simple terminal sliding-mode controller.