Intelligent Complementary Terminal Sliding Mode Using Multiloop Neural Network for Active Power Filter

This article presents a new intelligent sliding-mode control approach to effectively achieve harmonic suppression of an active power filter (APF). An intelligent complementary terminal sliding-mode controller is proposed to improve the control accuracy of current loop and deal with the lumped disturbances in the system. Complementary terminal sliding-mode control combines the features of complementary sliding-mode control and terminal sliding-mode control and does not depend on the accurate dynamic model. In addition, in order to reduce chattering, a multiloop neural network (MLNN), whose parameter learning laws of MLNN are derived based on the Lyapunov laws, is proposed to approximate the unknown nonlinear function term in the APF dynamic model, thus reducing the burden of sliding-mode control. Finally, within the computing power of control board, the detailed simulation and hardware experiments are carried out to demonstrate better harmonic suppression, and steady-state and dynamic performance compared with the existing methods.