Finite-Time Adaptive Fuzzy-Neural-Network Control of Active Power Filter

In this paper, an adaptive fuzzy-neural-network (AFNN) control using nonsingular terminal sliding mode control is proposed for active power filter (APF) as a current controller to attenuate the effect of unknown external disturbances and modeling uncertainties. First, the dynamic model for APF is built in which both the system parameter variations and external disturbance are considered. Then, a nonsingular terminal sliding mode control based on the backstepping (NTSMB) approach is presented for the current control system to solve singularity point problem and realize the fast and finite-time convergence. Moreover, AFNN is designed to relax the requirement of the prior knowledge of system parameters to improve the robustness of NTSMB. In the AFNN strategy, AFNN framework is designed to mimic the NTSMB, where the parameters are adjusted online by the adaptive law derived from the projection algorithm and the Lyapunov stability analysis, to guarantee tracking performance and stability of the closed-loop system. Simulation studies demonstrate that the proposed control methods exhibit excellent performance in both steady-state and transient operation compared to traditional sliding mode control. Experimental results are provided using a fully digital control system in order to validate the performance of the proposed controller.