Intelligent Global Sliding Mode Control Using Recurrent Feature Selection Neural Network for Active Power Filter
Shixi Hou, Yundi Chu, Juntao Fei
Abstract
This study develops an intelligent global sliding mode control using recurrent feature selection neural network for active power filter (APF). First, the dynamic model of an APF is constructed. Second, a conventional global sliding mode control (GSMC) is introduced to achieve the aim to track the quick changing reference signal for an APF current control strategy. Since uncertain parameters of APF are unavailable in advance, high performance current control cannot be assured in practical applications. In this article, to improve conventional GSMC for APF, the recurrent feature selection neural network (RFSNN) is proposed to learn uncertain function. Unlike the classical neural network, RFSNN can select useful network parameters and delete unfavorable network parameters to adjust the structure and parameters of the neural networks. Based on Lyapunov stability analysis, the online learning laws for network parameters are derived to satisfy the control objectives. Finally, the superiority and robustness of the proposed GSMC using RFSNN are verified by detailed experimental results.