Assessment of Novel Self-tuned Emotional Learning-based Intelligent Controller for Bidirectional Cascaded Quasi-Z-Source DC–DC Converter Control

Abstract

The self-tuned emotional learning-based intelligent controllers (STELBICs) are suggested to regulate a bidirectional cascaded quasi-Z-source DC-to-DC converters (BC-qZSCs). The bidirectional converters of DC-to-DC with a huge voltage conversion range are required in current times for voltage matching in a variety of applications, particularly electric automobiles. The BC-qZSC may raise or buck the voltage, and electricity flows in both directions. The switch’s duty cycle can be changed to keep the voltage steady. To produce a consistent output voltage, a good controller design is necessary. To enhance the performance of BC-qZSC in both operating modes, this study introduces a unique STELBIC. The proposed novel controller’s performance is evaluated by comparing it with an artificial neural networks-controlled converter and a traditional Proportional Integral-controlled converter. In both boost and buck modes of operation, the controller’s performance is evaluated in terms of voltage quality, including steady-state error, settling time, and voltage ripple. Matlab/Simulink is used to analyze the entire system.