Real-Time Nonlinear Model Predictive Control of Active Power Filter Using Self-Feedback Recurrent Fuzzy Neural Network Estimator

Abstract

A real-time nonlinear model predictive control using a self-feedback recurrent fuzzy neural network (SFRFNN) estimator for an active power filter is developed to improve the performance of harmonic compensation. First, an SFRFNN with a recurrent structure and fuzzy rules is proposed as a prediction model for nonlinear systems. The SFRFNN merges the advantages of the fuzzy system and the recurrent neural network with a self-feedback structure, which can significantly improve the dynamic performance. Second, the optimization method based on gradient descent is employed to solve the optimal control problem. In addition, the convergence of the proposed SFRFNN and the stability of RT-NMPC are guaranteed using Lyapunov stability theory. Finally, the hardware experiment demonstrated that the proposed method has better performance in both steady and dynamic states compared with existing methods and RT-NMPC using a radial basis function neural network.