Experimental Investigation of Recurrent Neural Network Fractional-Order Sliding Mode Control of Active Power Filter

Abstract

In this brief, a fractional-order sliding mode control (FSMC) scheme using a recurrent neural network (RNN) approximator is introduced to achieve better performance for a shunt active power filter (APF). The proposed RNNFSMC scheme combines a fractional-order sliding mode control method with a recurrent neural network structure. The fractional-order sliding mode control has more adjustable degree of freedom to brings more superior control effect than integer order sliding mode control. The RNN estimator is employed to approximate the unknown nonlinear function of the APF. Experimental results are presented to show the effectiveness of the proposed strategy, demonstrating the outstanding compensation performance and strong robustness compared with standard neural sliding mode controller.