Experimental Investigation on Buck Converter Using Neuro – Fuzzy Controller
AbstractBuck type DC-DC converter circuit topology is non-linear due to their switched circuit structure. Conventional control systems are insufficient to control non-linear systems. Neural Networks have important abilities such as learning, optimizing and adaptability. Fuzzy logic and neural networks are used as an adaptive structure based on the fuzzy logic controller. This adaptive structure adjusts the properties of the fuzzy rules and the characteristics of the control system so that the Neuro-Fuzzy controller can be adapted to all different system conditions. In this study, experimental studies were carried out on the dSPACE experiment platform to show the dynamic performance of the Neuro-Fuzzy controller and the conventional PI controller in different system conditions (such as reference voltage tracking and output load change) of buck type DC-DC converter.
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