Mario Norman Syah, Rizky Ajie Aprilianto, Agus Suryanto
The PID controller has been widely used for controlling interleaved buck converters in high-power applications. The mathematical estimation model of converters is employed to represent the transfer function of the system and use it to analyze the PID parameter values. However, obtaining an accurate transfer function model is crucial for optimal tuning parameters. In this study, the transfer function of the interleaved buck converter is estimated using the MATLAB Linearizer Toolbox. This paper investigated the performance of intelligent algorithms for tuning the PID controller parameters of the interleaved buck converter. The intelligent algorithms Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) tuned PID controller are evaluated through a comparison to the conventional tuning algorithm Ziegler-Nichols (ZN), Chien-Hrone-Reswick (CHR), and Approximate M-constrained Integral Gain Optimization (AMIGO). The evaluation includes transient step responses, time domain analysis, and disturbance rejection from load change condition. This study shows that PSO and GA enhance system performance by decreasing settling time, minimizing overshoot, and eliminating transient oscillation during load change conditions. These results offer tangible proof of the efficacy of employing intelligent algorithms to improve the operational efficiency of interleaved buck converters. © 2024 IEEE.
Universitas Negeri Semarang, Department of Electrical Engineering, Semarang, Indonesia
