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- W4361761206 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 super-twisting 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." @default.
- W4361761206 created "2023-04-04" @default.
- W4361761206 creator A5038383914 @default.
- W4361761206 creator A5080939291 @default.
- W4361761206 date "2023-08-01" @default.
- W4361761206 modified "2023-09-25" @default.
- W4361761206 title "Chebyshev Fuzzy Neural Network Super-Twisting Terminal Sliding-Mode Control for Active Power Filter" @default.
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- W4361761206 doi "https://doi.org/10.1109/jiot.2023.3262665" @default.
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