A Comparison of Fuzzy and Brain EmotionalLearning-Based Intelligent Control Approaches ForA Full Bridge Dc-Dc Converter

In this paper, a brain emotional learning-based intelligent controller (belbic) and a fuzzy controller were used to control the output voltage of a full bridge dc-dc converter. the converter was presented by its state space averaged model assuming that it operates in a continuous conduction mode (ccm). a comparison was also made between the results. the effectiveness of control approaches was demonstrated by the uncertainty of system parameters and acceptable load variations. the performance of the beblic and fuzzy controller in controlling the output voltage of the full bridge dc-dc converter was satisfactory. since these controllers are not designed to reduce error to zero, it is not possible to claim that the error rate is precisely zero. compared to the fuzzy controller, the belbic showed negligible overshoots and fluctuations. both controllers reached stabilization almost at once. it can, therefore, be concluded that the belbic outperforms the fuzzy controller given the uncertainty of the system parameters (including inductance, capacitance, and input voltage and acceptable variations of load).