Analog FOPID Controller Design for a Non-Ideal DC-DC Buck Converter Using a Novel Optimization Algorithm

This paper presents a novel approach to analog controller design for non-ideal DC-DC buck converters using an enhanced version of the Cooperation Search Algorithm (CSA). The proposed algorithm, called OCSANM, integrates Opposition-Based Learning (OBL) and the Nelder-Mead (NM) algorithm to overcome the limitations of traditional evolutionary algorithms. The OCSANM algorithm is used to optimize the parameters of a Fractional Order Proportional Integral Derivative (FOPID) controller, considering disturbance rejection effects and crossover frequency constraints. The implementation includes a comprehensive analog circuit design using operational amplifiers. Simulation results demonstrate that the OCSANM-FOPID controlled system achieves superior performance compared to both the conventional CSA algorithm and traditional pole placement method. Specifically, the proposed approach shows enhanced stability characteristics, improved disturbance rejection capabilities, and better transient response characteristics. These results validate the effectiveness of the proposed method for practical power electronics applications.