Chebyshev Fuzzy Neural Recursive Terminal Sliding Mode Control of Active Power Filter

A new finite time intelligent control strategy using an adaptive Chebyshev recurrent fuzzy neural network (ACRFNN) is proposed for the harmonic suppression of an active power filter (APF). Considering the nonlinear APF system encompasses the model uncertainties and the existence of lump external unknown disturbances in the controller design, a recursive terminal sliding mode controller (RTSMC) is proposed to improve the current loop tracking control accuracy and robustness for harmonic compensation of APF. And for the fast nonsingular terminal sliding mode surface and an integral mode surface integrated in the designed RTSMC, the tracking error achieves to converge in a finite time. In addition, to reduce the control burden of the RTSMC caused by the lump disturbances and mitigate chattering problem, a ACRFNN structurally combining recurrent neural network and Chebyshev neural network, is proposed as an approximator for the nonlinear dynamic model, and an adaptive node adjustment algorithm is introduced to the proposed neural network (NN) so that the NN structure can be further optimized. Hardware experiments are carried out to verify better performance of harmonic compensation property.