Complementary Sliding Mode Control Using Petri Probabilistic Fuzzy Recurrent Neural Network for Active Power Filter

Abstract

Current loop control of active power filter (APF) is vital for its harmonic suppression. In this paper, a complementary sliding mode controller (CSMC) with a petri probabilistic fuzzy recurrent neural network (PPFRNN) is designed for current control and harmonic suppression of an APF. Compared with the traditional sliding mode control (SMC), CSMC has less chattering and higher control accuracy. By combining the advantages of many kinds of networks, a new PPFRNN scheme is designed to estimate unknown nonlinear terms in the APF dynamic model, so as to reduce the chattering and further improve the performance of sliding mode controller. Simulation and hardware experiments proved the feasibility and superiority of the proposed method, showing it has better harmonic suppression, steady-state and dynamic performance compared with the existing methods. Note to Practitioners—This paper was motivated by the problem of power quality control using active power filter. a PPFRNN based ICSMC is proposed for harmonic suppression of APF. A CSMC is chosen due to the mathematical model of APF is difficult to be obtained in the practical application. However, the selection of parameter of traditional CSMC must balance the chattering problem and controller performance. Hence, a PPFRNN is introduced to reduce the burden of CSMC suppressing uncertainty of APF system, which can alleviate the contradiction between chattering problem and controller performance essentially. Finally, detail simulations and experiments verified the proposed ICSMC has a good harmonic suppression capability and small output chattering.