Feature Selection Fuzzy Neural Network Super-Twisting Harmonic Control

Abstract

This paper provides a multi-feedback feature selection fuzzy neural network (MFFSFNN) based on super-twisting sliding mode control (STSMC), aiming at compensating for current distortion and solving the harmonic current problem in an active power filter (APF) system. A feature selection layer is added to an output feedback neural network to attach the characteristics of signal filtering to the neural network. MFFSFNN, with the designed feedback loops and hidden layer, has the advantages of signal judging, filtering, and feedback. Signal filtering can choose valuable signals to deal with lumped uncertainties, and signal feedback can expand the learning dimension to improve the approximation accuracy. The STSMC, as a compensator with adaptive gains, helps to stabilize the compensation current. An experimental study is implemented to prove the effectiveness and superiority of the proposed controller.