Adaptive DC-output current regulation of basic series negative luo converter via uncertain dynamics: An interval type-2 PD-fuzzy logic control approach
Restricted accessResearch articleFirst published online 2025
Adaptive DC-output current regulation of basic series negative luo converter via uncertain dynamics: An interval type-2 PD-fuzzy logic control approach
This study aims to design a novel adaptive dc-output current regulator for a basic series negative output luo converter by means of an interval type-2 proportional + derivative fuzzy logic controller and to provide the desired transient response by using ellipsoid interval type-2 fuzzy membership functions with different uncertainty width ratios. In order to realize this aim, firstly, ellipsoid interval type-2 fuzzy membership functions with different uncertainty width ratios, which are closest to the human way of thinking, are selected. Then, the performances of these ellipsoid interval type-2 fuzzy membership functions with different uncertainty width ratios are adapted through the MacVicar-Whelan rule base to reduce the computational burden in terms of step dc-output current variation and step dc-input voltage variation using MATLAB/Simulink software. The dynamic performance of ellipsoid interval type-2 fuzzy membership functions with uncertain dynamics derived from different uncertainty width ratios were compared to each other and proportional + integral controller regarding three performance measures, namely rise time, settling time, and recovery time in detail. The simulation results demonstrated that the ellipsoid interval type-2 fuzzy membership function with a higher uncertainty width ratio (a1 = 1.6 and a2 = 0.4) improved average rise, settling and recovery time values by 68.59%, 76.24%, and 67.69%, respectively. Thus, it can be said that the ellipsoid interval type-2 fuzzy membership function with a higher uncertainty width ratio (a1 = 1.6 and a2 = 0.4) outperforms the proportional + integral controller and the other ellipsoid interval type-2 fuzzy membership functions with different uncertainty width ratios in terms of all performance measures.
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