Neural network-based model reference adaptive control of active power filter based on sliding mode approach

Model reference adaptive sliding mode control (MRASMC) using radical basis function (RBF) neural network (NN) is proposed to control the single-phase active power filter (APF). The RBF NN is utilized to approximate nonlinear function and eliminate the modeling error. AC side model reference adaptive current controller not only guarantees the globally stability of the APF system but also generate the compensating current to track the harmonic current accurately. Moreover, a sliding mode controller based on exponential approach is designed to improve the tracking performance of DC side voltage. Simulation results demonstrate that MRASMC using RBF NN can improve the adaptability and robustness of the APF system and track the given instructional signal quickly.