Fuzzy Neural Super-Twisting Sliding-Mode Control of Active Power Filter Using Nonlinear Extended State Observer

Abstract

To improve the tracking performance of the current controller of active power filter (APF) system, an adaptive super-twisting (ASTW) acrlong SMC using a nonlinear extended state observer (NESO) based on an interval type-2 fuzzy neural network (IT2FNN) strategy (ASTW- NESO) is proposed in this article. NESO based on IT2FNN is designed to estimate the system states and total disturbance, and then realize the active compensation of the total disturbance including unmodeled dynamics and external disturbances. Then, the ASTW adopts a special segmented dynamic adaptive gain super-twisting control to offset the remaining uncertainty and estimation error, and further weaken the system chattering. Simulation and experimental verification prove the designed controller not only has higher current compensation accuracy but also has smaller system chattering, showing better steady state and dynamic performance than the existing methods.