Comparative Study of Proportional–Integral, Fuzzy Logic, and Neural Fuzzy Logic Controllers for Boost Converter

In this chapter, a new control approach is proposed to improve the performance of a non-linear power electronic converter. The neuro-fuzzy controller is used to regulate the duty cycle of the boost converter. Unlike conventional PI controllers which are not compatible under the most critical conditions of maximum load, the neuro-fuzzy controller allows for a faster system response. A fuzzy logic controller is also implemented to regulate the duty cycle of the switching device for the boost converter and optimize the output voltage responsiveness. The generation of fuzzy rules and the adjustment of controller parameters are major challenges in fuzzy logic. However, thanks to the learning, optimization, and adaptation capabilities of neural networks, a combination of fuzzy logic and neural networks can be used to create a neuro-fuzzy controller based on an artificial neural dynamic network. The system is then simulated with the three types of controllers using MATLAB–Simulink. The objective of this study is to determine the most efficient controller for the boost converter, and the results obtained with the neuro-fuzzy controller are the most satisfactory with different references.