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Adaptive Backstepping Neural Control of Active Power Filter Using Complementary Sliding Mode Approach

This paper presents an adaptive backstepping radial basis function (RBF) neural network control scheme based on a complementary sliding mode method to improve the performance of harmonic current compensation of Active Power Filter (APF). Firstly, the control process is regulated by the back-stepping algorithm to simplify the procedure. On the basis of this, a complementary sliding surface is added to the traditional sliding one to reduce the time of reaching the sliding surface and improve the control accuracy. However, it is difficult to calculate the upper limit of the system uncertainties in practical applications. As a result, the RBF neural network controller is adopted to estimate the upper bound value of the uncertainties in the system and weaken the chattering phenomenon. Finally, an APF prototype based on Digital signal Processing (DSP), TMS320F28335 is built to verify the validity and effectiveness of the proposed algorithm.