Wavelet Fuzzy Neural Supertwisting Sliding Mode Control of an Active Power Filter

In this article, a wavelet fuzzy neural network controller with a supertwisting sliding mode controller (WFNNCSTSMC) is developed to control the harmonics and improve the power quality for an active power filter (APF). The merits of a supertwisting sliding mode controller (STSMC) and a wavelet fuzzy neural network (WFNN) controller are combined together, where the STSMC is utilized to approximate the output signal generated from the harmonic detection circuit in finite time and to ensure that the output is continuous. A wavelet layer is added to the structure of the fuzzy neural network to further improve the feature extraction ability. Because of its good effect in dealing with nonlinear terms, the WFNN controller is utilized to approach the equivalent controller to reduce switch gains and remove chattering. At the same time, parameter adaptive laws of WFNN are derived by the Lyapunov stability method to realize the fast training of WFNN. Finally, hardware experiment is accomplished to show the validity of the WFNNCSTSMC approach to control the current and harmonics under nonlinear loads and uncertainties.