Enhanced Dynamic Performance of FSBB Converters Using EPSO-Tuned FOPI Control: From Simulation to Hardware Validation

The four-switch buck–boost (FSBB) converter is widely adopted in photovoltaic power conversion systems owing to its wide voltage regulation capability and high efficiency. Nevertheless, under wide operating conditions and abrupt disturbances, conventional proportional–integral (PI) and fractional-order proportional–integral (FOPI) controllers struggle to simultaneously achieve fast transient response, high steady-state accuracy, and strong disturbance rejection, particularly in dual-loop control architectures with strongly coupled parameters. To address these challenges, this paper proposes an enhanced particle swarm optimization (EPSO) strategy for systematic tuning of the six parameters of a dual-loop FOPI controller in an FSBB converter. The proposed EPSO integrates chaotic initialization, opposition-based learning, Lévy flight exploration, and Cauchy mutation to form a coarse-to-fine search mechanism tailored for high-dimensional and nonlinear controller optimization. A composite objective function combining the integral of time-weighted absolute error (ITAE), overshoot penalty, and steady-state error is formulated to ensure balanced dynamic and static performance.Comprehensive simulation studies and hardware experiments are conducted to validate the proposed approach. Comparative results with PI and PSO-PI controllers demonstrate that the EPSO-FOPI scheme consistently achieves reduced overshoot, shorter settling time, and enhanced disturbance suppression under load and output voltage variations. Moreover, the close agreement between experimental waveforms and simulation results confirms the practical feasibility and robustness of the proposed method. These results indicate that EPSO-based FOPI tuning provides an effective and implementable solution for improving the dynamic performance and reliability of FSBB-based photovoltaic power conversion systems.