Novel Neural Network Fractional-Order Sliding-Mode Control With Application to Active Power Filter
Juntao Fei, Huan Wang, Yunmei Fang
Abstract
In this article, a fractional-order sliding-mode control scheme based on a two-hidden-layer recurrent neural network (THLRNN) is proposed for a single-phase shunt active power filter. Considering the shortcomings of traditional neural networks (NNs) that the approximation accuracy is not high and weight and center vector of NNs are unchangeable, a new THLRNN structure which contains two hidden layers to make the network have more powerful fitting ability, is designed to approximate the unknown nonlinearities. A fractional-order term is added to a sliding-mode controller to have more adjustable space and better optimization space. Simulation and experimental studies prove that the proposed THLRNN strategy can accomplish the current compensation well with acceptable current tracking error, and have satisfactory compensation property and robustness compared with a traditional neural sliding controller.