Chebyshev Fuzzy Neural Network Super-Twisting Terminal Sliding-Mode Control for Active Power Filter

Abstract

To suppress the harmonic pollution, a global fast super-twisting terminal sliding mode controller (GFSTTSMC) using an adaptive recurrent Chebyshev fuzzy neural network (ARCFNN) is proposed to eliminate the current distortion for an active power filter (APF) with unknown model nonlinearities. First, a global fast terminal sliding mode controller (GFTSMC) is adopted due to its advantages in finite-time convergence and faster convergence rate of tracking error. Second, a supertwisting sliding mode control (STSMC) algorithm having an edge in terms of weakening the chattering and smoothening the input signals, is designed to promote the dynamic tracking capability of APF. Third, by combining the fuzzy neural network and Chebyshev polynomials, the ARCFNN is utilized to estimate the unknown model of the APF system due to its strong generalization and approximation ability. Ultimately, the availability of the ARCFNN-GFSTTSMC scheme has been fully numerically and experimentally verified in an APF prototype, showing the improvement of characteristic performance than other strategies.