Adaptive Sliding Mode Long Short-Term Memory Fuzzy Neural Control for Harmonic Suppression

In this paper, an adaptive sliding mode controller based on a long and short-term memory fuzzy neural network (ASMC-LSTMFNN) is proposed to suppress harmonics for an active power filter (APF). Firstly, a mathematical dynamic model of a single-phase shunt active power filter considering lumped uncertainties is introduced. Then, based on the design of conventional sliding mode control (SMC), a new type of long and short-term memory fuzzy neural network (LSTMFNN) is proposed to approximate the unknown function of the system. The LSTMFNN incorporates a fuzzy neural network (FNN) structure and long and short-term memory (LSTM) mechanism, excellent learning ability and approximation performance. Moreover, the parameters of the neural network are all automatically adjusted through the adaptive laws, and the Lyapunov stability theorem guarantees the current tracking performance and the stability of the closed-loop system. Finally, hardware experiments are carried out based on the dSPACE hardware platform, and the experimental results show that it has good steady-state and dynamic performance, verifying that it has better control performance and harmonic compensation ability compared with the adaptive sliding mode control based on recurrent fuzzy neural network (ASMC-RFNN).

Abstract licence: CC BY-NC-ND

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