Robust Intelligent Control for a Class of Power-Electronic Converters Using Neuro-Fuzzy Learning Mechanism

This article considers a robust intelligent control problem for a class of power-electronic converters via a neuro-fuzzy learning mechanism. First, a terminal sliding-mode control (TSMC) is designed to ensure finite-time error convergence and further enhance the system performance. Meanwhile, a saturation function is utilized in the proposed TSMC. Then, by using type-2 fuzzy neural network (T2FNN) to approximate the developed TSMC, the corresponding adaptive T2FNN controller with online parameter adjustment is established. To enhance the generalization ability for the uncertainties, the recurrent feature-selection algorithm is added into T2FNN. Moreover, the existence of adaptive compensator comprised by upper bound updated law can avoid the impact of the approximation error. Finally, to show the superiorities of the T2FNN controller, it is applied to active power filter, and the simulation and experimental results are compared with the existing literature.