Comparative Analysis of Fuzzy and Neural Controller for a Boost Converter

DC-DC converters are often used in electrical systems to keep the output voltage constant. Boost converters are utilized for a variety of purposes, including regenerative braking of direct current motors, portable device applications, and regulated power supplies. Buck converters, on the other hand, are used in sophisticated communications, data communication, and self-regulating power supplies. One of the most crucial elements in the power conversion process is managing the DC-DC converters. This study aims to find out which nonlinear controller, fuzzy or neural network, works best when the output load or boost converter characteristics change. So, in this, we create a boost converter controller using a fuzzy and neural network. The fuzzy controller for this study's converter employs a standard set of rules, whereas the neural network controller employs two hidden layer networks. MATLAB software is then used to reconstruct both controllers. The simulation results show that the fuzzy logic controller has a very long transient and settling period with no steady-state error in both transient and steady-state situations. On the other hand, the neural network controller has a short transient and settling period with a steady-state error.