Double-Hidden-Layer Recurrent Neural Network Fractional-Order Sliding Mode Control of Shunt Active Power Filter

Abstract

In this paper, a fractional-order sliding mode control scheme based on a double-hidden-layer recurrent neural network is proposed for a single-phase shunt active power filter. Considering the shortcomings of traditional neural networks that the approximation accuracy is not high and weight and center vector of neural networks are unchangeable, a new double-hidden-layer recurrent neural network structure which contains two hidden layers to make the network have more powerful fitting ability, is designed to approximate the unknown nonlinearities. An original output feedback neural network with two hidden layers is designed to estimate the uncertainties regardless of unknown system characteristics and external disturbances. A fractional-order term is added to the sliding mode controller to have more adjustable space and better optimization space. Experimental results verified the validity of the designed controller and proved that it can complete the current compensation well with acceptable current tracking error, demonstrating the outstanding compensation performance and strong robustness