Model reference adaptive sliding mode control using RBF neural network for active power filter

• A model reference adaptive sliding mode controller using RBF NN is proposed.
• The RBF NN is utilized to approximate and eliminate the modeling error.
• An exponential sliding controller is designed for DC side voltage control.
• Simulation demonstrates the effectiveness of the proposed method.

In this paper, a model reference adaptive sliding mode (MRASMC) using a 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 the nonlinear function and eliminate the modeling error in the APF system. The model reference adaptive current controller in AC side not only guarantees the globally stability of the APF system but also the compensating current to track the harmonic current accurately. Moreover, a sliding mode voltage controller based on an exponential approach law is designed to improve the tracking performance of DC side voltage. Simulation results demonstrate strong robustness and outstanding compensation performance with the proposed APF control system. In conclusion, MRASMC using RBF NN can improve the adaptability and robustness of the APF system and track the given instructional signal quickly.