Self-Feedback Neural Network Sliding Mode Control With Extended State Observer for Active Power Filter

In this article, a fuzzy neural network adaptive sliding mode control with a self-feedback recursion (FNNASMC-SFR)-based linear extended state observer (LESO) is proposed for a single-phase active power filter (APF), where the adaptive sliding mode controller is designed to improve the response and accuracy of current compensation and reference current tracking. The LESO is designed to estimate the actual APF system dynamics which includes the parameter perturbation and external disturbance. Moreover, the fuzzy neural network with self-feedback recursion is adopted to mimic the switching control gain of adaptive sliding mode controller, which combines the output values of neurons at the current time and the previous time, to achieve better dynamic approximation effect and prevent sudden changes. Simulation and hardware experiments verify the introduced method is a viable control solution in harmonics suppression and current control.