Particle Swarm Optimization for Fuzzy Adaptive Sliding Mode Control in Half-Bridge Buck Converter: A Comparative Analysis

Abstract

The primary objective of the control system in a half-bridge DC-DC buck converter is to maintain a constant output voltage through a feedback mechanism, regardless of input voltage or load variations. This paper presents a comparative analysis of several advanced control strategies, including Sliding Mode Control (SMC), Adaptive Sliding Mode Control (ASMC), and Fuzzy Adaptive Sliding Mode Control (FASMC), with a particular focus on optimizing ASMC and FASMC using Particle Swarm Optimization (PSO). The PSO algorithm is employed to fine-tune key controller parameters to mitigate chattering, improve transient response, and enhance overall system efficiency. Results demonstrate a significant reduction in steady-state output voltage ripple and improved stability under varying load conditions and line disturbances. The proposed converter and control strategies are evaluated across six distinct scenarios designed to assess performance under different operating environments. While all controllers were tested under identical conditions, ASMC and FASMC exhibited superior tracking capabilities. Among them, FASMC achieved the best overall performance in terms of response speed, chattering suppression, and energy efficiency. For benchmarking purposes, classical Proportional-Integral-Derivative (PID) and Model Predictive Control (MPC) methods were also implemented. However, these enhancements are specifically accomplished through the implementation of the FASMC control strategy, which efficiently mitigates the output voltage ripple and improves system stability.