Advanced controller design based on interval type-2 fuzzy neural network for elementary super-lift Luo converter

Abstract

The elementary super-lift Luo converter is a third-order DC/DC step-up converter developed using the super-lift method that increases input voltage with low current and voltage ripples and a high voltage gain. On account of the nonlinearity and uncertainty incorporated with voltage, load changes, and switching operation of elementary super-lift Luo (ESLL) converter, it is a challenging task to design an efficient controller. To overcome these problems, this study proposes a new control approach based on interval type-2 fuzzy neural network (IT2FNN) to regulate a DC output current and voltage of ESLL converter. The parameters of IT2FNN were trained offline using a gradient descent algorithm. Mean square error (MSE), one of the most used statistical performance indicators, was used to evaluate the convergence accuracy performance of gradient descent algorithm. The DC output voltage regulation performance of IT2FNN was evaluated via a comparative simulation with interval type-2 fuzzy controller (IT2FC) and proportional + integral (PI) controller in MATLAB/Simulink environment in terms of settling time, average computational time, and recovery time under three different operational conditions: various reference voltages, input voltage, and load. Moreover, the DC output current regulation performance of IT2FNN was evaluated via a comparative simulation with IT2FC and PI controller in MATLAB/Simulink environment in terms of settling time and steady-state error under various reference current changes. The detailed simulation results obtained from comparative simulation validated the effectiveness of IT2FNN.