Comparison of Fuzzy and Brain Emotional Learning Based Intelligent Control approaches for a Full Bridge DC-DC Converter

Abstract

In this paper, the Brain Emotional Learning Based Intelligent Controller (BELBIC) and fuzzy controller were used to control output voltage of the full bridge DC-DC converter. The converter is presented by its state space averaged model assuming that it operates in the continuous conduction mode (CCM). A comparison was also made between the results. The effectiveness of control approaches are 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 shows negligible overshoots and fluctuations. Both controllers reach stabilization almost at once. It is, therefore, concluded that the BELBIC acts better than the fuzzy controller. Considering the uncertainty of system parameters (including inductance, capacitance, and input voltage and acceptable variations of load), BELBIC acts better than the fuzzy controller.