Performance Comparison of Optimization Algorithm Tuned PID Controllers in Positive Output Re-Lift Luo Converter Operation for Electric Vehicle Applications

Abstract

In this work, a Particle Swarm-Based optimization algorithm is proposed to optimize the PID controller used in the Positive Output Re-Lift Luo converter for the Electric Vehicle application. The novelty of the work lies in developing an improved PID controller to explore the reliability of the re-lift Luo converter. In addition, the optimization improves the effectiveness of the closed-loop system that possesses high voltage gain with esteemed power density and efficiency. With the Particle Swarm Optimization control algorithm, , , and values are tuned in such a way to obtain the appropriate steady-state condition with no peak overshoot or undershoot, much lesser settling time, no steady-state error, and improved dynamic performances. To analyze the system stability and to obtain better results, reduced-order state-space modeling of the Re-lift Luo converter is adopted, which offers high computational speed and accuracy. Initially, a PID controller is designed using the Zeigler Nicholas method, and the results are compared with the genetic algorithm and Particle Swarm Optimization algorithm. The time-domain analysis has been carried out to analyze and compare the converter’s performance. The simulation results were verified experimentally, and the results prove the robustness of the proposed converter with a PID controller optimized using the Particle Swarm Optimization algorithm.